WO2019019375A1 - 核保决策树的创建方法、装置、计算机设备及存储介质 - Google Patents

核保决策树的创建方法、装置、计算机设备及存储介质 Download PDF

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WO2019019375A1
WO2019019375A1 PCT/CN2017/104598 CN2017104598W WO2019019375A1 WO 2019019375 A1 WO2019019375 A1 WO 2019019375A1 CN 2017104598 W CN2017104598 W CN 2017104598W WO 2019019375 A1 WO2019019375 A1 WO 2019019375A1
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attribute
sub
underwriting
decision tree
sample training
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French (fr)
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邵正铂
李斌
陈杰
高雪
马向东
丁杰
张捷
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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Priority to SG11201810237YA priority patent/SG11201810237YA/en
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2228—Indexing structures
    • G06F16/2246—Trees, e.g. B+trees
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms

Definitions

  • the present application relates to the field of insurance technology, and in particular, to a method, an apparatus, a computer device, and a storage medium for creating a policy for ensuring a policy.
  • the underwriting work for the user's insurance policy is mainly manual review.
  • the basis for the audit is based on the insured's relevant information on the one hand and the reviewer's work experience on the other hand, but the work experience for the individual is after all. Limited, if there is no corresponding intuitive historical data as a reference, it is difficult to accurately review the user's policy.
  • a method for creating a check guarantee decision tree, a computer device, and a storage medium are provided.
  • a method for creating a nuclear guarantee decision tree comprising: acquiring a sample training set including different sample attributes; calculating an entropy value of each attribute indicating a result of the underwriting result according to the underwriting result of the sample of each attribute in the sample training set; Gain; the attribute with the highest entropy gain is used as the current node of the underwriting decision tree, and the sub-attribute corresponding to the attribute with the highest entropy gain is divided into the next node of the current node; the divided part is extracted from the sample training set a sample training subset of sub-attributes; determining the sample training subset as the sample training set, recursively calculating the entropy gain and dividing operation of the sub-attribute The sub-attribute of the next node until the division meets the preset condition of the leaf node that becomes the underwriting decision tree.
  • a device for creating a check guarantee decision tree comprising: a sample acquisition module, configured to acquire a sample training set including different sample attributes; and an entropy value gain calculation module, configured to perform underwriting of samples according to each attribute of the sample training set As a result, an entropy gain that affects the underwriting result in each attribute is calculated; a node partitioning module is used to use the attribute with the highest entropy gain as the current node of the underwriting decision tree, and the sub-correspondence of the attribute with the highest entropy gain The attribute is divided into a next node of the current node; a subset extracting module is configured to extract the divided sample training subset of the sub-attribute from the sample training set; and a recursive module, configured to determine the sample training subset as a The sample training set recursively calculates the entropy gain and division operation on the sub-attribute until the sub-attribute of the divided next node satisfies a preset condition
  • a computer device comprising a memory and one or more processors, the memory storing computer readable instructions, the computer readable instructions being executed by the processor, causing the one or more processors to execute The following steps: obtaining a sample training set including different sample attributes; calculating an entropy value gain indicating the effect of the underwriting result in each attribute according to the underwriting result of the samples of each attribute in the sample training set; and using the attribute having the highest entropy gain as the core Preserving the current node of the decision tree, and dividing the sub-attribute corresponding to the attribute with the highest entropy gain as the next node of the current node; extracting the sample training subset of the sub-attribute from the sample training set; The sample training subset is determined as the sample training set, and the entropy gain and the dividing operation are recursively calculated for the sub-attribute until the sub-attribute of the divided next node satisfies a preset condition of the leaf node that becomes the under
  • One or more non-transitory readable storage mediums storing computer readable instructions, when executed by one or more processors, cause the one or more processors to perform the steps of: acquiring a sample training set including different sample attributes; according to the underwriting result of the samples of each attribute in the sample training set, calculating an entropy value gain representing the underwriting result in each attribute; and the attribute having the highest entropy gain as the underwriting decision tree a current node, and dividing the sub-attribute corresponding to the attribute with the highest entropy gain as the next node of the current node; extracting the sample training subset of the sub-attribute from the sample training set; and training the sample training subset Determined as the sample training set, the entropy gain and the dividing operation are recursively calculated for the sub-attribute until the sub-attribute of the divided next node satisfies a preset condition of the leaf node that becomes the underwriting decision tree.
  • FIG. 1 is a flow chart of a method for creating a check guarantee tree in an embodiment
  • FIG. 2 is a flowchart of a method for creating a check guarantee decision tree in another embodiment
  • FIG. 3 is a flow chart of a method for creating a check guarantee decision tree in still another embodiment
  • FIG. 4 is a schematic diagram of a usage scenario in an embodiment
  • FIG. 5 is a block diagram showing an exemplary structure of a device for creating a check guarantee tree in an embodiment
  • FIG. 6 is a schematic diagram showing the internal structure of a computer device in an embodiment.
  • FIG. 1 is a flowchart of a method for creating a policy-preserving decision tree according to an embodiment of the present application.
  • a method for creating a policy-preserving decision tree according to an embodiment of the present application is described in detail below with reference to FIG. 1 , as shown in FIG. 1 .
  • the method includes the following steps S101, S102, S103, S104, and S105.
  • the source of the sample training set is the sample data selected from the historical underwriting record, and the sample data selected from the historical underwriting record is used as a basis for creating the underwriting decision tree for the reviewer's work. More instructive.
  • the above attributes include at least two of the following conditions: age, industry risk, past medical history, and loss ratio, wherein the sub-attributes of the age attribute include the young, the old, and the middle age.
  • the sub-attributes of the attribute of the industry risk include high risk, low risk and medium risk.
  • the sub-attributes of the previous medical history attribute include yes and no.
  • the sub-attributes of the attribute of the loss ratio include high loss ratio and low loss ratio.
  • the sample training set acquired according to an example of the present embodiment is as shown in the following Table (1):
  • the age range of each age group can be set by the actual business demand. According to an example of the embodiment, for example, 0 to 25 years old can be set to a young age, and 26 to 45 years old can be set to a medium age. The age of 46 years and older is set to the age of seniority.
  • the step S102 specifically includes: extracting the underwriting result of the sample of the same attribute from the sample training set, and calculating the entropy value gain of the attribute according to the underwriting result of the same attribute.
  • the underwriting result in step S102 includes whether the underwriting pass and the corresponding genus
  • the entropy gain is calculated by the following formula: the nuclear pass rate and the underwriting rate:
  • M is the total underwriting pass rate in the sample training set
  • a i is the ratio of the number of sub-attributes i corresponding to attribute A to the total number in the sample training set
  • B i is the nucleus whose sub-attribute i is based on the number of attributes A
  • the guaranteed pass rate, n represents the number of sub-attributes corresponding to the attribute A
  • G A represents the entropy value gain of the calculated attribute A.
  • the total entropy value of the sample training set may be calculated according to the sample training set, and then the entropy value of one of the attributes in the sample training set may be calculated, and then the decision entropy value and the sample training set are
  • the difference of the entropy value of an attribute is taken as the entropy value gain of the attribute.
  • the significance of the entropy value gain is that it can express the influence of the attribute on the underwriting result. The greater the entropy value gain, the greater the influence on the underwriting result.
  • the underwriting result of a sample with reference to an attribute such as the age extracted in the above sample training set is as shown in the following table (2):
  • the sub-attribute i of the attribute A includes middle age, young age and old age, and can be obtained according to the above table (1) and table (2):
  • the sub-attributes of the age attribute are the proportion of the younger age to the total number of sample training sets.
  • the sub-attribute is the underwriting rate based on the number of attributes A.
  • the sub-attribute is the underwriting rate based on the number of attributes A.
  • the decision entropy value S G can be calculated:
  • the entropy value of the sub-attribute of middle age can be calculated to be 0.9157, and the entropy value of the attribute of old age is 0, and then the entropy value of age attribute A is calculated by the following formula (2):
  • the entropy value of the age attribute A can be calculated as:
  • the entropy gains of industry risk, past medical history, and loss ratio can be calculated to be 0.0176, 0.1726, and 0.0453, respectively.
  • the attribute with the highest entropy gain is used as the current node of the underwriting decision tree, and the sub-attribute corresponding to the attribute with the highest entropy gain is divided into the next node of the current node.
  • the node facilitates the underwriting personnel to conduct a key review of the attributes of the upper nodes in the underwriting decision tree, which is conducive to improving the accuracy of the underwriting.
  • the attribute of age is taken as the current node of the underwriting decision tree.
  • the corresponding sub-attribute of the age attribute includes a young age, an old age, and a middle age, which is extracted according to a usage scenario of the embodiment.
  • the sample training subset of a sub-attribute is shown in Table (3) below:
  • Age-low age Industry insurance Past medical history Payout ratio Whether the guarantee is passed count Young age high no high by 640 Young age high no low by 640 Young age in no low by 1280 Young age in Yes low Fail 640 Young age low Yes high Fail 640
  • S105 Determine a sample training subset as a sample training set, and recursively calculate the entropy gain and the dividing operation on the sub-attribute until the sub-attribute of the divided next node satisfies a preset condition of the leaf node that becomes the underwriting decision tree. .
  • the recursive operation in the step refers to determining the corresponding sub-attribute as the attribute A in the above formula (1), and calculating the entropy value of the sub-attribute of the attribute A according to the extracted sample training subset.
  • the gain is divided into branches in the underwriting decision tree until the sub-attribute of the next node of the divided partition satisfies the preset condition of the leaf node that becomes the underwriting decision tree.
  • the table (3) is determined as the sample training set, and the attribute of the young age is determined as the attribute A in the above formula (1), one by one. Calculate the entropy gain of the attributes of industry insurance, past medical history, and loss ratio, and recursively until the sub-attribute of the next node of the division satisfies the preset condition of the leaf node that becomes the underwriting decision tree.
  • step S104 and the recursive operation of step S105 are performed for each sub-attribute under the age attribute until the sub-attribute of the next node in the division satisfies the leaf that becomes the underwriting decision tree.
  • the preset condition of the node is the following condition of the node.
  • the entropy gain of each attribute in the sample training set is calculated, and the attribute with the largest entropy gain is used as the root node of the underwriting decision tree, and then the intermediate node attribute and the leaf of the underwriting decision tree are divided by recursion.
  • the node attribute is used to create a policy-guaranteed decision tree based on each attribute, so that the underwriter can perform an important review on the attributes of the underlying layer of the underwriting decision tree, such as the root node, according to the underwriting decision tree, to provide the user with the basis for underwriting Improve the accuracy of underwriting.
  • FIG. 2 is a flowchart of a method for creating a check guarantee decision tree according to another embodiment of the present application. As shown in FIG. 2, the method for creating a check guarantee decision tree includes the above steps S101 to S104, and the foregoing steps are performed. S105 further includes the following step S201.
  • S201 Determine a sample training subset as a sample training set, and recursively calculate the entropy gain and division operation on the sub-attribute until the sub-attribute of the divided sub-attribute has only one or the sub-attribute of the sub-attribute is passed or not passed or When the entropy value gain of the sub-attribute is less than a preset threshold, the sub-attribute is determined as a leaf node of the underwriting decision tree.
  • the sub-attribute corresponding to the entropy value gain less than the preset threshold may be pruned, and the sub-attribute is A node attribute is used as the leaf node of the underwriting decision tree.
  • FIG. 4 is a schematic diagram of a usage scenario according to an embodiment of the present application.
  • the determined usage scenario of a leaf node according to the present embodiment is as shown in FIG. 4, when the sub-attribute old age leaf node's underwriting result is not passed.
  • the sub-attribute of the old age is determined as the leaf node of the underwriting decision tree.
  • the attributes of the root node to the leaf node of the underwriting decision tree are sequentially age-low age--in the past medical history, the core of the past medical history The pass rate is not passed, and the non-existing medical history is not passed. Therefore, the past medical history can be used as the leaf node of the branch of the age-lower age-previous medical history in the underwriting decision tree.
  • the attribute of the root node to the leaf node of the underwriting decision tree divided by the above recursive algorithm is age-low age-industry risk Medium - If the sub-attribute of the previous medical history only includes the loss ratio, the sub-attribute loss ratio can be used as the leaf node of the underwriting decision tree.
  • the attribute of the root node to the leaf node of the underwriting decision tree divided by the recursive algorithm described above is age-low age-industry risk--the past medical history is no, wherein the past If the entropy gain of the medical history is less than the preset threshold, the past medical history can be used as the leaf node of the underwriting decision tree, or the leaf node of the past medical history can be pruned, and the last node industry of the leaf node is Risk is the leaf node of the underwriting decision tree.
  • the sub-attribute with small entropy gain is used as a pruning operation, and the attribute having little influence on the underwriting decision can be excluded from the underwriting decision tree, thereby further improving the underwriting decision presented by the underwriting decision tree.
  • the accuracy is used as a pruning operation, and the attribute having little influence on the underwriting decision can be excluded from the underwriting decision tree, thereby further improving the underwriting decision presented by the underwriting decision tree.
  • FIG. 3 is a flowchart of a method for creating a policy-preserving decision tree according to still another embodiment of the present application. As shown in FIG. 3, the method for creating a policy-preserving decision tree further includes the following steps including the foregoing steps S101 to S105. Step S301.
  • the underwriting result in the step may be the number of sub-attributes and the number of unqualified passes as shown in FIG. 4, and may also be the underwriting of the corresponding sub-attribute of the leaf node. Pass rate and / or underwriting rate.
  • a method for automatically performing underwriting by using a check-proof decision tree comprising: acquiring each attribute in a policy to be insured, and obtaining each attribute and a policy of the underwriting decision tree The attributes of each node are matched, and the underwriting result corresponding to the leaf node in the attribute matching the leaf node of the underwriting decision tree is used as the underwriting result of the policy.
  • the step of matching the acquired attributes with the attributes of the nodes of the underwriting decision tree further includes: obtaining attributes of the current node of the underwriting decision tree, and the same as the current node attribute of the policy to be verified
  • the attribute is determined to be successfully matched with the attribute of the current node, and further obtains the sub-attribute of the attribute in the policy that matches the current node successfully, and then queries the same sub-attribute attribute in the underwriting decision tree, and further in the sub-attribute
  • the other attributes of the attribute are matched with the intermediate nodes of the underwriting decision tree until the leaf nodes of the underwriting decision tree are matched, and the underwriting result of the leaf node is used as the underwriting result of the pending insurance policy.
  • the current node to the leaf node in one of the branches of the underwriting decision tree are: age-low age-high industry risk, wherein the leaf node attribute--the underwriting of high industry risk
  • the results are not passed. If the age attribute in the policy to be insured belongs to the age range, the past medical history is yes, the industry risk is high, then the age is matched to the sub-attribute of the underwriting decision tree, and then the core is obtained. The next node in the decision-making tree is the industry risk, and the industry risk in the policy to be underwritten is matched to the low-level industry risk high in the decision tree, and then the industry risk is determined to be the underwriting. If the leaf node of the decision tree does not pass the check result, the attribute of the past medical history in the policy to be insured does not need to be matched, and the policy to be insured can be directly verified. The decision passed.
  • the labels of the foregoing steps S101-S301 are not used to limit the sequence of the steps in the embodiment, and the numbers of the steps are only for the convenience of referring to the labels of the steps when describing the steps. It is to be noted that as long as the order in which the steps are performed does not affect the logical relationship of the embodiment, it is considered to be within the scope of protection of the present application.
  • FIG. 5 is an exemplary structural block diagram of a device for creating a policy-guaranteed decision tree according to an embodiment of the present application.
  • FIG. 5 is a detailed description of a device for creating a policy-guaranteed decision tree according to an embodiment of the present application, as shown in FIG. 5.
  • the apparatus for creating a check guarantee tree includes: a sample acquisition module 11 for acquiring a sample training set including different sample attributes; and an entropy value gain calculation module 12 for training samples of each attribute according to the sample The underwriting result is calculated, and the entropy value gain indicating the influence of the underwriting result in each attribute is calculated; the node dividing module 13 is configured to use the attribute with the highest entropy gain as the current node of the underwriting decision tree, and the attribute with the highest entropy gain The corresponding sub-attribute is divided into a next node of the current node; the subset extracting module 14 is configured to extract the divided sample training subset of the sub-attribute from the sample training set; the recursive module 15 is configured to use the sample training subset Determining as a sample training set, recursively calculating the entropy gain and division operation on the sub-attribute until the sub-attribute of the divided next node satisfies the compulsory decision tree Preconditions
  • the sample obtaining module is specifically configured to use the sample data selected from the historical underwriting record, and the sample data selected from the historical underwriting record is used as a basis for creating the underwriting decision tree for the reviewer's work. More instructive.
  • the entropy value gain calculation module 12 is further configured to extract the underwriting result of the sample of the same attribute from the sample training set, and then calculate the attribute according to the underwriting result of the same attribute. Entropy gain.
  • the entropy value gain calculation module 12 is further configured to: first calculate a total decision entropy value S G of the sample training set according to the sample training set, and then calculate one of the attributes in the sample training set.
  • the entropy value S A and then the difference between the decision entropy value and the entropy value of one of the attributes in the sample training set is taken as the entropy value gain of the attribute.
  • the significance of the entropy value gain is that the influence of the attribute on the underwriting result can be expressed. The greater the entropy gain, the greater the impact on the underwriting results.
  • the entropy value of each sub-attribute of attribute A is S Ai :
  • the entropy gain calculation module calculates the entropy gain by the following formula:
  • M is the total underwriting pass rate in the sample training set
  • a i is the ratio of the number of sub-attributes i corresponding to attribute A to the total number in the sample training set
  • B i is the nucleus whose sub-attribute i is based on the number of attributes A
  • the guaranteed pass rate, n represents the number of sub-attributes corresponding to the attribute A
  • G A represents the entropy value gain of the calculated attribute A.
  • the attribute includes at least two of the following conditions: age, industry risk, past medical history and claims ratio, wherein the sub-attributes of the attribute of age include young age, old age and middle age, and the sub-attributes of the attribute of the industry risk include high Risk, low risk and medium risk, the sub-attributes of the previous medical history attribute include yes and no, the sub-attributes of the loss ratio attribute include high loss ratio and low loss ratio.
  • the recursive module 15 is specifically configured to determine the corresponding sub-attribute as the attribute A in the formula (1), calculate the entropy value gain of the sub-attribute of the attribute A according to the extracted sample training subset, and perform the underwriting decision tree.
  • the node dividing module 13 takes the attribute having the largest entropy gain as the core.
  • the current node of the decision-making tree is beneficial to the underwriting personnel to conduct a key review of the attributes on the upper layer of the underwriting decision tree, which is conducive to improving the accuracy of the underwriting.
  • the recursive module 15 further includes: a first leaf node determining unit, configured to determine the sub-attribute as a leaf node of the underwriting decision tree when the divided sub-attribute has only one; or a two-leaf node determining unit, configured to determine the sub-attribute as a leaf node of the under-guaranteed decision tree when the sub-attribute result of the divided sub-attribute is passed or not; or a third leaf node determining unit, configured to: When the entropy value gain of the sub-attribute is less than a preset threshold, the sub-attribute is determined as a leaf node of the underwriting decision tree.
  • the third leaf node determining unit is further configured to: when the entropy value gain of the sub-attribute is less than a preset threshold, cut a sub-attribute corresponding to an entropy value gain that is less than a preset threshold. Branch operation, the last node attribute of the sub-attribute is used as the leaf node of the underwriting decision tree.
  • the sub-attribute of the old age is determined as the leaf node of the underwriting decision tree.
  • the attributes of the root node to the leaf node of the underwriting decision tree are sequentially age-low age--in the past medical history, the core of the past medical history The pass rate is not passed, and the non-existing medical history is not passed. Therefore, the past medical history can be used as the leaf node of the branch of the age-lower age-previous medical history in the underwriting decision tree.
  • Another usage scenario according to the present embodiment such as the root node of the underwriting decision tree divided by the above recursive algorithm, to the attribute of a branch of the leaf node is in turn age-low age-industry risk--the past medical history is no
  • the sub-attribute loss ratio can be used as the leaf node of the underwriting decision tree.
  • the attribute of the root node to the leaf node of the underwriting decision tree divided by the recursive algorithm described above is age-low age-industry risk--the past medical history is no, wherein the past If the entropy gain of the medical history is less than the preset threshold, the past medical history can be used as the leaf node of the underwriting decision tree, or the leaf node of the past medical history can be pruned, and the last node industry of the leaf node is Risk is the leaf node of the underwriting decision tree.
  • the verification decision tree creation device 10 further includes:
  • a display module configured to display the underwriting decision tree and display the underwriting result of the corresponding attribute in the leaf node of the underwriting decision tree.
  • the display module is specifically configured to display the number of sub-property guarantees and the number of unqualified passes, and may also be the pass rate of the sub-attributes of the leaf nodes. And/or the rate of failure of underwriting.
  • first, second, and third leaf node determining unit are only in that different leaf node determining units are added. The distinction is not used to define which leaf node determines the priority of the unit or other limited meaning.
  • the various modules in the above-described creation device of the underwriting decision tree may be implemented in whole or in part by software, hardware, and combinations thereof.
  • the above modules may be embedded in the hardware of the terminal or may be stored in the memory of the terminal in a software form, so that the processor calls the execution of the operations corresponding to the above modules.
  • the processor can be a central processing unit (CPU), a microprocessor, a microcontroller, or the like.
  • the apparatus for creating the above-described underwriting decision tree can be implemented in the form of a computer readable instruction running on a computer device as shown in FIG.
  • a computer device the internal structure of which may correspond to the structure of FIG. 6, that is, the computer device may be either a server or a terminal, including a memory and one or more processors.
  • a next node extracting a sample training subset of the divided sub-attributes from the sample training set; and determining the sample training subset as the sample training set, recursively calculating the entropy gain and the dividing operation on the sub-attribute until the next node of the division
  • the sub-attribute satisfies the preset condition of the leaf node that becomes the underwriting decision tree.
  • the performing, by the processor, the step of calculating the entropy gain affecting the underwriting result in each attribute according to the underwriting result of the samples of each attribute in the sample training set including:
  • the entropy gain is calculated by the following formula:
  • M is the total underwriting pass rate in the sample training set
  • a i is the ratio of the number of sub-attributes i corresponding to attribute A to the total number in the sample training set
  • B i is the nucleus whose sub-attribute i is based on the number of attributes A
  • the guaranteed pass rate, n represents the number of sub-attributes corresponding to the attribute A
  • G A represents the entropy value gain of the calculated attribute A.
  • the step of determining whether the sub-attribute performed by the processor satisfies a preset condition of the leaf node of the underwriting decision tree comprises: determining the sub-attribute as the underwriting decision when there is only one sub-attribute of the divided sub-attribute The leaf node of the tree; or when the sub-attribute of the divided sub-attribute passes or fails, the sub-attribute is determined as the leaf node of the under-guaranteed decision tree; or when the entropy gain of the sub-attribute is less than a preset threshold , the sub-attribute is determined as the leaf node of the underwriting decision tree.
  • the sample training subset is determined by the processor as a sample training set, and the entropy gain and the partitioning operation are recursively computed for the sub-attributes until the sub-attribute of the divided next node satisfies the underwriting decision tree.
  • the processor executing the computer readable instructions is further for performing the steps of: displaying the underwriting decision tree and displaying the underwriting result of the corresponding attribute in the leaf node of the underwriting decision tree.
  • the attributes include at least two of the following: age, industry risk, past medical history, and claims ratio.
  • FIG. 6 is a schematic diagram showing the internal structure of a computer device according to an embodiment of the present application, which may be a server.
  • the computer device includes a processor coupled through a system bus, a non-volatile storage medium, an internal memory, an input device, and a display screen.
  • the non-volatile storage medium of the computer device can store an operating system and computer readable instructions that, when executed, cause the processor to perform the creation of a underwriting decision tree of various embodiments of the present application
  • the processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device.
  • the internal memory can store computer readable instructions that, when executed by the processor, cause the processor to perform a method of creating a blanket decision tree.
  • the input device of the computer device is used for input of various parameters, and the display screen of the computer device is used for display. It will be understood by those skilled in the art that the structure shown in FIG. 6 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation of the computer device to which the solution of the present application is applied.
  • the specific computer device may It includes more or fewer components than those shown in the figures, or some components are combined, or have different component arrangements.
  • one or more non-volatiles storing computer readable instructions are provided A readable storage medium, when executed by one or more processors, causes one or more processors to perform the steps of: acquiring a sample training set comprising different sample attributes; and nucleating samples of each attribute according to the sample training set Assume the result, calculate the entropy gain that affects the underwriting result in each attribute; use the attribute with the highest entropy gain as the current node of the underwriting decision tree, and divide the sub-attribute corresponding to the attribute with the highest entropy gain into the current node.
  • a next node extracting a sample training subset of the divided sub-attributes from the sample training set; and determining the sample training subset as the sample training set, recursively calculating the entropy gain and the dividing operation on the sub-attribute until the next node of the division
  • the sub-attribute satisfies the preset condition of the leaf node that becomes the underwriting decision tree.
  • the step of calculating, by the processor, the entropy gain representing the inclusive result based on the underwriting result of the samples of each attribute in the sample training set including: calculating the entropy gain by the following formula:
  • M is the total underwriting pass rate in the sample training set
  • a i is the ratio of the number of sub-attributes i corresponding to attribute A to the total number in the sample training set
  • B i is the nucleus whose sub-attribute i is based on the number of attributes A
  • the guaranteed pass rate, n represents the number of sub-attributes corresponding to the attribute A
  • G A represents the entropy value gain of the calculated attribute A.
  • the step of determining whether the sub-attribute performed by the processor satisfies a preset condition of the leaf node of the underwriting decision tree comprises: determining the sub-attribute as the underwriting decision when there is only one sub-attribute of the divided sub-attribute The leaf node of the tree; or when the sub-attribute of the divided sub-attribute passes or fails, the sub-attribute is determined as the leaf node of the under-guaranteed decision tree; or when the entropy gain of the sub-attribute is less than a preset threshold , the sub-attribute is determined as the leaf node of the underwriting decision tree.
  • the sample training subset is determined by the processor as a sample training set, and the entropy gain and the partitioning operation are recursively computed for the sub-attributes until the sub-attribute of the divided next node satisfies the underwriting decision tree.
  • the processor executing the computer readable instructions is further for performing the steps of: displaying the underwriting decision tree and displaying the underwriting result of the corresponding attribute in the leaf node of the underwriting decision tree.
  • the attributes include at least two of the following: age, industry risk, past medical history, and claims ratio.
  • all or part of the processes in the foregoing embodiment may be completed by using computer readable instructions to instruct related hardware, and the program may be stored in a computer readable storage medium, such as
  • the program can be stored in a storage medium of the computer system and executed by at least one processor in the computer system to implement a process including an embodiment of the methods described above.
  • the storage medium includes, but is not limited to, a magnetic disk, a USB flash drive, an optical disk, a read-only memory (ROM), and the like.
  • the entropy value gain of each attribute in the sample training set is calculated, and the attribute with the largest entropy value gain is used as the current node of the underwriting decision tree, and then the intermediate node attribute and the leaf of the underwriting decision tree are divided by recursion.
  • the node attribute is used to create a policy-guaranteed decision tree based on each attribute, so that the underwriter can perform an important review on the attributes of the underlying layer of the underwriting decision tree, such as the root node, according to the underwriting decision tree, and enable the underwriter to
  • the underwriting result displayed in the leaf node in the underwriting decision tree directly makes the underwriting decision to provide the user with the data basis for the underwriting, improving the accuracy of the underwriting and the efficiency of the underwriting.

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Abstract

一种核保决策树的创建方法，该方法包括：获取包括不同样本属性的样本训练集（S101）；根据该样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值（102）；将熵值最高的属性作为该核保决策树的当前节点，并将该熵值最高的属性对应的子属性划分为该当前节点的下一节点（S103）；从该样本训练集中抽取划分的该子属性的样本训练子集（S104）；将该样本训练子集确定为该样本训练集，对该子属性递归计算该熵值增益及划分操作，直到该划分的下一节点的子属性满足成为该核保决策树的叶子节点的预设条件（105）。

Description

核保决策树的创建方法、装置、计算机设备及存储介质
本申请要求于2017年7月26日提交中国专利局，申请号为201710618080.0，发明名称为“核保决策树的创建方法、装置、计算机设备及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及保险技术领域，特别是涉及一种核保决策树的创建方法、装置、计算机设备及存储介质。
背景技术
在保险这一领域中，经常需要对用户的投保单进行核保，依据核保人的年龄、所从事的行业、收入、性别的等相关信息审核对应的保单是否可以承保。
目前对于用户的投保单的核保工作主要是人工来审核，审核的依据一方面是基于被保人的相关信息，另一方面还基于审核人的工作经验，但是对于个人而言其工作经验毕竟有限，如果没有对应的直观历史数据作为参考难以对用户的保单进行准确的审核。
发明内容
根据本申请的各种实施例，提供一种核保决策树的创建方法、计算机设备及存储介质。
一种核保决策树的创建方法，该方法包括：获取包括不同样本属性的样本训练集；根据该样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益；将熵值增益最高的属性作为核保决策树的当前节点，并将该熵值增益最高的属性对应的子属性划分为该当前节点的下一节点；从该样本训练集中抽取划分的该子属性的样本训练子集；将所述样本训练子集确定为所述样本训练集，对该子属性递归计算上述熵值增益及划分操 作，直到划分的下一节点的子属性满足成为该核保决策树的叶子节点的预设条件。
一种核保决策树的创建装置，该装置包括：样本获取模块，用于获取包括不同样本属性的样本训练集；熵值增益计算模块，用于根据该样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益；节点划分模块，用于将熵值增益最高的属性作为核保决策树的当前节点，并将该熵值增益最高的属性对应的子属性划分为该当前节点的下一节点；子集抽取模块，用于从该样本训练集中抽取划分的该子属性的样本训练子集；递归模块，用于将所述样本训练子集确定为所述样本训练集，对该子属性递归计算上述熵值增益及划分操作，直到划分的下一节点的子属性满足成为该核保决策树的叶子节点的预设条件。
一种计算机设备，包括存储器和一个或多个处理器，所述存储器中存储有计算机可读指令，所述计算机可读指令被所述处理器执行时，使得所述一个或多个处理器执行以下步骤：获取包括不同样本属性的样本训练集；根据该样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益；将熵值增益最高的属性作为核保决策树的当前节点，并将该熵值增益最高的属性对应的子属性划分为该当前节点的下一节点；从该样本训练集中抽取划分的该子属性的样本训练子集；将所述样本训练子集确定为所述样本训练集，对该子属性递归计算上述熵值增益及划分操作，直到划分的下一节点的子属性满足成为该核保决策树的叶子节点的预设条件。
一个或多个存储有计算机可读指令的非易失性可读存储介质，所述计算机可读指令被一个或多个处理器执行时，使得所述一个或多个处理器执行以下步骤：获取包括不同样本属性的样本训练集；根据该样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益；将熵值增益最高的属性作为核保决策树的当前节点，并将该熵值增益最高的属性对应的子属性划分为该当前节点的下一节点；从该样本训练集中抽取划分的该子属性的样本训练子集；将所述样本训练子集确定为所述样本训练集，对该子属性递归计算上述熵值增益及划分操作，直到划分的下一节点的子属性满足成为该核保决策树的叶子节点的预设条件。
本申请的一个或多个实施例的细节在下面的附图和描述中提出。本申请的其它特征、目的和优点将从说明书、附图以及权利要求书变得明显。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。
图1为一个实施例中核保决策树的创建方法的流程图；
图2为另一实施例中核保决策树的创建方法的流程图；
图3为又一实施例中核保决策树的创建方法的流程图；
图4为一个实施例中使用场景示意图；
图5为一个实施例中核保决策树的创建装置的示范性结构框图；
图6为一个实施例中计算机设备的内部结构示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
图1为根据本申请的一个实施例的核保决策树的创建方法的流程图，下面结合图1来详细描述根据本申请的一个实施例的核保决策树的创建方法，如图1所示，该方法包括以下步骤S101、S102、S103、S104及S105。
S101、获取包括不同样本属性的样本训练集。
根据本实施例的一个示例，该样本训练集的来源为从历史核保记录中选取的样本数据，将从历史核保记录中选取的样本数据作为创建核保决策树的依据对于审核人的工作具有更强的指导性。
在该步骤中，上述的属性包括以下情况中的至少两种：年龄、行业风险、既往病史及赔付率，其中年龄这一属性的子属性包括低龄、高龄和中龄，行 业风险这一属性的子属性包括高风险、低风险和中风险，既往病史这一属性的子属性包括是和否，赔付率这一属性的子属性包括赔付率高和赔付率低。
根据本实施例的一个示例获取的样本训练集如下表(1)所示：
年龄 行业风险 既往病史 赔付率 是否核保通过 计数
低龄 高 否 高 通过 640
高龄 高 否 高 不通过 1280
中龄 中 否 高 不通过 600
低龄 高 否 低 通过 640
中龄 低 是 高 不通过 640
中龄 低 是 低 通过 640
高龄 低 是 低 不通过 640
低龄 中 否 低 通过 1280
中龄 中 是 高 不通过 1320
低龄 中 是 低 不通过 640
高龄 中 否 低 不通过 320
低龄 低 是 高 不通过 640
高龄 高 是 高 不通过 320
中龄 中 否 低 通过 630
中龄 中 否 低 不通过 10
表(1)
其中，各年龄段的年龄区间可以通过实际业务的需求的来设定，根据本实施例的一个示例例如：可以将0～25岁设为低龄，可以将26～45岁设为中龄，可以将46岁及以上岁数以上设为高龄。
S102、根据该样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益。
根据本实施例的一个示例，该步骤S102具体还包括：从该样本训练集中抽取同一属性的样本的核保结果，再根据同一属性的核保结果计算该属性的熵值增益。
在一个实施例中，该步骤S102中的核保结果包括是否核保通过及对应属 性的核保通过率和核保不通过率，通过以下公式计算熵值增益：
Figure PCTCN2017104598-appb-000001
其中，M表示样本训练集中总核保通过率，Ai表示对应属性A的子属性i的数量占样本训练集中的总数量的比例，Bi表示子属性i以属性A的数量为基数的核保通过率，n表示对应属性A的子属性的个数，GA表示计算的属性A的熵值增益。
在一个实施例中，可以依据该样本训练集先计算出该样本训练集总的决策熵值，再计算出样本训练集中的其中一个属性的熵值，然后将决策熵值与样本训练集中的其中一个属性的熵值的差值作为该属性的熵值增益，熵值增益的意义在于可以表示该属性对于核保结果的影响力，熵值增益越大表示对于核保结果的影响力度越大。
根据本实施例的一个使用场景，参照例如在上述样本训练集中抽取的年龄这一属性的样本的核保结果如下表(2)所示：
年龄 核保决定 数量
中龄 通过 1270
中龄 不通过 2570
低龄 通过 2560
低龄 不通过 1280
高龄 不通过 2560
高龄 通过 0
表(2)
根据上述表(1)可以得出：
总的通过率
Figure PCTCN2017104598-appb-000002
总的不通过率
Figure PCTCN2017104598-appb-000003
当上述属性A表示年龄这一属性时，该属性A的子属性i包括中龄、低龄和高龄，根据上述表(1)和表(2)可以得出：
年龄属性的子属性为低龄的数量占样本训练集中的总数量的比例
Figure PCTCN2017104598-appb-000004
该子属性低龄以属性A的数量为基数的核保通过率
Figure PCTCN2017104598-appb-000005
该子属性低龄以属性A的数量为基数的核保不通过率
Figure PCTCN2017104598-appb-000006
可以计算出决策熵值SG：
Figure PCTCN2017104598-appb-000007
还可以计算出低龄这一子属性的熵值SAi：
Figure PCTCN2017104598-appb-000008
同理，可以计算出中龄这一子属性的熵值为0.9157，老龄这一属性的熵值为0，然后利用以下公式(2)计算出年龄属性A的熵值：
Figure PCTCN2017104598-appb-000009
可以计算出年龄属性A的熵值大小为：
Figure PCTCN2017104598-appb-000010
再通过上述公式(1)可以得出年龄这一属性的熵值增益GA为：
GA＝0.9537-0.6877＝0.2660。
同理，参照上述表(1)可计算出行业风险、既往病史、赔付率的熵值增益分别为0.0176、0.1726和0.0453。
S103、将熵值增益最高的属性作为核保决策树的当前节点，并将该熵值增益最高的属性对应的子属性划分为该当前节点的下一节点。
由于熵值增益的意义在于可以表示该属性对于核保结果的影响力，熵值增益越大表示对于核保结果的影响力度越大，将熵值增益最大的属性作为该核保决策树的当前节点有利于核保人员对于处于该核保决策树上层节点的属性进行重点审核，有利于提高核保的准确性。
根据本实施例的使用场景例如，当通过上述步骤S102的步骤计算的年龄这一属性的熵值增益最高的属性时，则将年龄这一属性作为该核保决策树的当前节点。
S104、从该样本训练集中抽取划分的该子属性的样本训练子集。
根据本实施例的一个示例，当上述熵值增益最高的属性为年龄这一属性，年龄属性中对应的子属性包括低龄、高龄和中龄，根据本实施例的一个使用场景抽取出来的低龄这一子属性的样本训练子集如下表(3)所示：
年龄-低龄 行业分险 既往病史 赔付率 是否核保通过 计数
低龄 高 否 高 通过 640
低龄 高 否 低 通过 640
低龄 中 否 低 通过 1280
低龄 中 是 低 不通过 640
低龄 低 是 高 不通过 640
表(3)
S105、将样本训练子集确定为样本训练集，对该子属性递归计算上述熵值增益及划分操作，直到划分的下一节点的子属性满足成为该核保决策树的叶子节点的预设条件。
根据本实施例的一个示例，该步骤中的递归操作是指将对应的子属性确定为上述公式(1)中的属性A，依据抽取的样本训练子集，计算属性A的子属性的熵值增益并进行核保决策树中分支的划分，直到划分出的划分的下一节点的子属性满足成为该核保决策树的叶子节点的预设条件。
根据本实施例的一个使用场景参照表(3)所示，即将上述表(3)确定为上述样本训练集，将低龄这一属性确定为上述公式(1)中的属性A，逐一 计算行业分险、既往病史、赔付率这些属性的熵值增益，依次递归直到划分的下一节点的子属性满足成为该核保决策树的叶子节点的预设条件。
在一个实施例中，对年龄属性下的每个子属性都进行上述步骤S104的抽取操作和步骤S105中的递归操作，直到其中的划分的下一节点的子属性满足成为该核保决策树的叶子节点的预设条件。
本实施例通过计算样本训练集中各个属性的熵值增益，并将熵值增益最大的属性作为核保决策树的根节点，再通过递归的方式划分出该核保决策树的中间节点属性以及叶子节点属性，以创建基于各个属性的核保决策树，使得核保人可以依据该核保决策树对处于该核保决策树例如根节点等上层的属性进行着重审核，以给用户提供核保依据，提高核保的准确性。
图2为根据本申请的另一实施例的核保决策树的创建方法的流程图，如图2所示，该核保决策树的创建方法在包括上述步骤S101至S104的基础上，上述步骤S105进一步包括以下步骤S201。
S201、将样本训练子集确定为样本训练集，对该子属性递归计算上述熵值增益及划分操作，直到划分的子属性只有一个或划分的子属性的核保结果均为通过或者不通过或当该子属性的熵值增益小于预设的阈值时，将该子属性确定为该核保决策树的叶子节点。
根据本实施例的一个示例，当该子属性的熵值增益小于预设的阈值时，还可以对小于预设的阈值的熵值增益对应的子属性做剪枝操作，将该子属性的上一节点属性作为该核保决策树的叶子节点。
图4为根据本申请的一个实施例的使用场景示意图，根据本实施例的叶子节点的确定的使用场景如图4所示，当子属性老年这一叶子节点的核保结果均为不通过时，将老年这一子属性确定为该核保决策树的叶子节点。根据本实施例的另一使用场景参照上述表(3)，例如核保决策树的根节点至叶子节点的某一个分支的属性依次为年龄——低龄——既往病史中，是既往病史的核保通过率均为不通过，非既往病史均为不通过，因此可以将既往病史作为该核保决策树中年龄——低龄——既往病史这一分支的叶子节点。
根据本实施例的另一使用场景例如通过上述递归算法划分的核保决策树的根节点至叶子节点的某一个分支的属性依次为年龄——低龄——行业风险 中——既往病史否下的子属性仅包括赔付率时，该子属性赔付率可作为核保决策树的叶子节点。
根据本实施例的又一使用场景通过上述递归算法划分的核保决策树的根节点至叶子节点的某一个分支的属性依次为年龄——低龄——行业风险中——既往病史否，其中既往病史的熵值增益如果小于预设的阈值，则可将既往病史作为该核保决策树的叶子节点，也可将既往病史这一叶子节点做剪枝操作，将该叶子节点的上一节点行业风险作为该核保决策树的叶子节点。
本实施例将熵值增益很小的子属性做剪枝操作，可以将对核保决策影响很小的属性剔除在核保决策树之外，进一步提高了核保决策树所呈现的核保决策的准确性。
图3为根据本申请的又一实施例的核保决策树的创建方法的流程图，如图3所示，该核保决策树的创建方法在包括上述步骤S101至S105的基础上还包括以下步骤S301。
S301、显示该核保决策树并在该核保决策树的叶子节点中显示对应属性的核保结果。
根据本实施例的一个示例，该步骤中的核保结果可以是如图4中所示子属性核保通过的数量和核保不通过的数量，还可以是该叶子节点对应子属性的核保的通过率和/或核保的不通过率。
根据本申请的一个实施例还提供了一种利用核保决策树自动进行核保的方法，该方法包括：获取待核保的保单中的各属性，将获取的各属性与核保决策树的各个节点的属性进行匹配，将与该核保决策树的叶子节点匹配成功的属性中该叶子节点所对应的核保结果作为该保单的核保结果。
其中，将获取的各属性与核保决策树的各个节点的属性进行匹配的步骤进一步包括：获取该核保决策树的当前节点的属性，将该待核保的保单中与当前节点属性相同的属性确定为与当前节点的属性匹配成功，进一步获取该保单中与当前节点匹配成功的属性的子属性，然后在核保决策树中查询与获取的子属性相同的分支属性，进一步再在该子属性的其它属性中依次与核保决策树的中间节点进行匹配，直到匹配到该核保决策树的叶子节点，将该叶子节点的核保结果作为该待核保保单的核保结果。
根据本实施例的一个使用场景例如，核保决策树的其中一个分支中从当前节点到叶子节点依次为：年龄——低龄——行业风险高，其中叶子节点属性——行业风险高的核保结果为均不通过，如果待核保的保单中的年龄属性属于低龄的范围，既往病史为是，行业风险为高，则依次将年龄匹配到核保决策树的子属性低龄中，然后获取核保决策树中低龄的下一节点为行业风险高，将待核保的保单中的行业风险为高这一属性匹配到保决策树中低龄的行业风险高中，然后确定行业风险高为该核保决策树的叶子节点，且该叶子节点的核保结果为均不通过，则不需要再匹配待核保的保单中的既往病史这一属性，可以直接作出该待核保的保单核保不通过的决定。
根据本实施例的一个示例，上述步骤S101～S301的标号并不用于限定本实施例中各个步骤的先后顺序，各个步骤的编号只是为了使得描述各个步骤时可以通用引用该步骤的标号进行便捷的指代，只要各个步骤执行的顺序不影响本实施例的逻辑关系均视为在本申请的保护范围之内。
图5为根据本申请的一个实施例的核保决策树的创建装置的示范性结构框图，下面结合图5来详细描述根据本申请的一个实施例的核保决策树的创建装置，如图5所示，该核保决策树的创建装置10包括：样本获取模块11，用于获取包括不同样本属性的样本训练集；熵值增益计算模块12，用于根据该样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益；节点划分模块13，用于将熵值增益最高的属性作为核保决策树的当前节点，并将该熵值增益最高的属性对应的子属性划分为该当前节点的下一节点；子集抽取模块14，用于从该样本训练集中抽取划分的该子属性的样本训练子集；递归模块15，用于将样本训练子集确定为样本训练集，对该子属性递归计算上述熵值增益及划分操作，直到划分的下一节点的子属性满足成为该核保决策树的叶子节点的预设条件。
根据本实施例的一个示例，上述样本获取模块具体用于从历史核保记录中选取的样本数据，将从历史核保记录中选取的样本数据作为创建核保决策树的依据对于审核人的工作具有更强的指导性。
在一个实施例中，上述熵值增益计算模块12具体还用于从该样本训练集中抽取同一属性的样本的核保结果，再根据同一属性的核保结果计算该属性 的熵值增益。
根据本实施例的一个示例，上述熵值增益计算模块12具体还用于：依据该样本训练集先计算出该样本训练集总的决策熵值SG，再计算出样本训练集中的其中一个属性的熵值SA，然后将决策熵值与样本训练集中的其中一个属性的熵值之差作为该属性的熵值增益，熵值增益的意义在于可以表示该属性对于核保结果的影响力，熵值增益越大表示对于核保结果的影响力度越大。
其中，SG＝-(M×log2M+(1-M)log2 (1-M))；
Figure PCTCN2017104598-appb-000011
其中，属性A的每个子属性的熵值为SAi：
Figure PCTCN2017104598-appb-000012
在一个实施例中，该熵值增益计算模块通过以下公式计算熵值增益：
Figure PCTCN2017104598-appb-000013
其中，M表示样本训练集中总核保通过率，Ai表示对应属性A的子属性i的数量占样本训练集中的总数量的比例，Bi表示子属性i以属性A的数量为基数的核保通过率，n表示对应属性A的子属性的个数，GA表示计算的属性A的熵值增益。
其中，该属性包括以下情况中的至少两种：年龄、行业风险、既往病史及赔付率，其中年龄这一属性的子属性包括低龄、高龄和中龄，行业风险这一属性的子属性包括高风险、低风险和中风险，既往病史这一属性的子属性包括是和否，赔付率这一属性的子属性包括赔付率高和赔付率低。
其中，上述递归模块15具体用于将对应的子属性确定为上述公式(1)中的属性A，依据抽取的样本训练子集，计算属性A的子属性的熵值增益并进行核保决策树中分支的划分，直到划分出的划分的下一节点的子属性满足成为该核保决策树的叶子节点的预设条件。
由于熵值增益的意义在于可以表示该属性对于核保结果的影响力，熵值增益越大表示对于核保结果的影响力度越大，上述节点划分模块13将熵值增益最大的属性作为该核保决策树的当前节点有利于核保人员对于处于该核保决策树上层的属性进行重点审核，有利于提高核保的准确性。
根据本实施例的一个示例，该递归模块15还包括：第一叶子节点确定单元，用于当划分的子属性只有一个时，将该子属性确定为该核保决策树的叶子节点；或第二叶子节点确定单元，用于当划分的子属性的核保结果均为通过或者不通过时，将该子属性确定为该核保决策树的叶子节点；或第三叶子节点确定单元，用于当该子属性的熵值增益小于预设的阈值时，将该子属性确定为该核保决策树的叶子节点。
根据本实施例的另一示例，该第三叶子节点确定单元还用于当该子属性的熵值增益小于预设的阈值时，对小于预设的阈值的熵值增益对应的子属性做剪枝操作，将该子属性的上一节点属性作为该核保决策树的叶子节点。
根据本实施例的一个使用场景例如当子属性老年这一叶子节点的核保结果均为不通过时，将老年这一子属性确定为该核保决策树的叶子节点。根据本实施例的另一使用场景参照上述表(3)，例如核保决策树的根节点至叶子节点的某一个分支的属性依次为年龄——低龄——既往病史中，是既往病史的核保通过率均为不通过，非既往病史均为不通过，因此可以将既往病史作为该核保决策树中年龄——低龄——既往病史这一分支的叶子节点。
根据本实施例的另一使用场景例如通过上述递归算法划分的核保决策树的根节点至叶子节点的某一个分支的属性依次为年龄——低龄——行业风险中——既往病史否下的子属性仅包括赔付率时，该子属性赔付率可作为核保决策树的叶子节点。
根据本实施例的又一使用场景通过上述递归算法划分的核保决策树的根节点至叶子节点的某一个分支的属性依次为年龄——低龄——行业风险中——既往病史否，其中既往病史的熵值增益如果小于预设的阈值，则可将既往病史作为该核保决策树的叶子节点，也可将既往病史这一叶子节点做剪枝操作，将该叶子节点的上一节点行业风险作为该核保决策树的叶子节点。
根据本实施例的一个示例，该核保决策树的创建装置10还包括：
显示模块，用于显示该核保决策树并在该核保决策树的叶子节点中显示对应属性的核保结果。
根据本实施例的一个示例，该显示模块具体用于显示子属性核保通过的数量和核保不通过的数量，还可以是该叶子节点对应子属性的核保的通过率 和/或核保的不通过率。
其中上述第一叶子节点确定单元、第二叶子节点确定单元及第三叶子节点确定单元中的“第一”、“第二”和“第三”的意义仅在于将不同的叶子节点确定单元加以区分，并不用于限定哪个叶子节点确定单元的优先级更高或者其它的限定意义。
上述核保决策树的创建装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于终端的存储器中，也可以以软件形式存储于终端的存储器中，以便于处理器调用执行以上各个模块对应的操作。该处理器可以为中央处理单元(CPU)、微处理器、单片机等。
上述核保决策树的创建装置可以实现为一种计算机可读指令的形式，在如图6所示的计算机设备上运行。
在一个实施例中，提供了一种计算机设备，计算机设备的内部结构可对应于如图6的结构，即该计算机设备既可以是服务器也可以是终端，包括存储器和一个或多个处理器，存储器中存储有计算机可读指令，计算机可读指令被处理器执行时，使得一个或多个处理器执行以下步骤：获取包括不同样本属性的样本训练集；根据样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益；将熵值增益最高的属性作为核保决策树的当前节点，并将熵值增益最高的属性对应的子属性划分为当前节点的下一节点；从样本训练集中抽取划分的子属性的样本训练子集；及将样本训练子集确定为样本训练集，对子属性递归计算熵值增益及划分操作，直到划分的下一节点的子属性满足成为核保决策树的叶子节点的预设条件。
在一个实施例中，处理器所执行的根据样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益的步骤，包括：
通过以下公式计算熵值增益：
Figure PCTCN2017104598-appb-000014
其中，M表示样本训练集中总核保通过率，Ai表示对应属性A的子属性i的数量占样本训练集中的总数量的比例，Bi表示子属性i以属性A的数量为 基数的核保通过率，n表示对应属性A的子属性的个数，GA表示计算的属性A的熵值增益。
在一个实施例中，处理器所执行的判断子属性是否满足成为核保决策树的叶子节点的预设条件的步骤，包括：当划分的子属性只有一个时，将子属性确定为核保决策树的叶子节点；或当划分的子属性的核保结果均为通过或者不通过时，将子属性确定为核保决策树的叶子节点；或当子属性的熵值增益小于预设的阈值时，将子属性确定为核保决策树的叶子节点。
在一个实施例中，在处理器所执行的将样本训练子集确定为样本训练集，对子属性递归计算熵值增益及划分操作，直到划分的下一节点的子属性满足成为核保决策树的叶子节点的预设条件的步骤之后，处理器执行计算机可读指令还用于实现以下步骤：显示核保决策树并在核保决策树的叶子节点中显示对应属性的核保结果。
在一个实施例中，属性包括以下情况中的至少两种：年龄、行业风险、既往病史及赔付率。
图6为根据本申请的一个实施例的计算机设备的内部结构示意图，该计算机设备可以为服务器。参照图6，该计算机设备包括通过系统总线连接的处理器、非易失性存储介质、内存储器、输入装置和显示屏。其中，该计算机设备的非易失性存储介质可存储操作系统和计算机可读指令，该计算机可读指令被执行时，可使得处理器执行本申请各实施例的一种核保决策树的创建方法，该方法的具体实现过程可参考图1至4各实施例的具体内容，在此不再赘述。该计算机设备的处理器用于提供计算和控制能力，支撑整个计算机设备的运行。该内存储器中可储存有计算机可读指令，该计算机可读指令被处理器执行时，可使得处理器执行一种核保决策树的创建方法。计算机设备的输入装置用于各个参数的输入，计算机设备的显示屏用于进行显示。本领域技术人员可以理解，图6中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，提供了一个或多个存储有计算机可读指令的非易失性 可读存储介质，计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行以下步骤：获取包括不同样本属性的样本训练集；根据样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益；将熵值增益最高的属性作为核保决策树的当前节点，并将熵值增益最高的属性对应的子属性划分为当前节点的下一节点；从样本训练集中抽取划分的子属性的样本训练子集；及将样本训练子集确定为样本训练集，对子属性递归计算熵值增益及划分操作，直到划分的下一节点的子属性满足成为核保决策树的叶子节点的预设条件。
在一个实施例中，处理器所执行的根据样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益的步骤，包括：通过以下公式计算熵值增益：
Figure PCTCN2017104598-appb-000015
其中，M表示样本训练集中总核保通过率，Ai表示对应属性A的子属性i的数量占样本训练集中的总数量的比例，Bi表示子属性i以属性A的数量为基数的核保通过率，n表示对应属性A的子属性的个数，GA表示计算的属性A的熵值增益。
在一个实施例中，处理器所执行的判断子属性是否满足成为核保决策树的叶子节点的预设条件的步骤，包括：当划分的子属性只有一个时，将子属性确定为核保决策树的叶子节点；或当划分的子属性的核保结果均为通过或者不通过时，将子属性确定为核保决策树的叶子节点；或当子属性的熵值增益小于预设的阈值时，将子属性确定为核保决策树的叶子节点。
在一个实施例中，在处理器所执行的将样本训练子集确定为样本训练集，对子属性递归计算熵值增益及划分操作，直到划分的下一节点的子属性满足成为核保决策树的叶子节点的预设条件的步骤之后，处理器执行计算机可读指令还用于实现以下步骤：显示核保决策树并在核保决策树的叶子节点中显示对应属性的核保结果。
在一个实施例中，属性包括以下情况中的至少两种：年龄、行业风险、既往病史及赔付率。
根据本实施例的一个示例，上述实施例方法中的全部或部分流程，可以通过计算机可读指令来指令相关的硬件来完成，所述程序可存储于一计算机可读取存储介质中，如本申请实施例中，该程序可存储于计算机系统的存储介质中，并被该计算机系统中的至少一个处理器执行，以实现包括如上述各方法的实施例的流程。该存储介质包括但不限于磁碟、优盘、光盘、只读存储记忆体(Read-Only Memory，ROM)等。
本实施例通过计算样本训练集中各个属性的熵值增益，并将熵值增益最大的属性作为核保决策树的当前节点，再通过递归的方式划分出该核保决策树的中间节点属性以及叶子节点属性，以创建基于各个属性的核保决策树，使得核保人可以依据该核保决策树对处于该核保决策树例如根节点等上层的属性进行着重审核，并使得核保人可以依据核保决策树中叶子节点中显示的核保结果直接作出核保决定，以给用户提供核保的数据依据，提高核保的准确性和核保的效率。
以上所述实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (20)

  1. 一种核保决策树的创建方法，包括：
    获取包括不同样本属性的样本训练集；
    根据所述样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益；
    将熵值增益最高的属性作为核保决策树的当前节点，并将所述熵值增益最高的属性对应的子属性划分为所述当前节点的下一节点；
    从所述样本训练集中抽取划分的所述子属性的样本训练子集；及
    将所述样本训练子集确定为所述样本训练集，对所述子属性递归计算所述熵值增益及划分操作，直到划分的下一节点的子属性满足成为所述核保决策树的叶子节点的预设条件。
  2. 根据权利要求1所述的方法，其特征在于，所述根据所述样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益，包括：
    通过以下公式计算所述熵值增益：
    Figure PCTCN2017104598-appb-100001
    其中，M表示样本训练集中总核保通过率，Ai表示对应属性A的子属性i的数量占样本训练集中的总数量的比例，Bi表示所述子属性i以所述属性A的数量为基数的核保通过率，n表示对应属性A的子属性的个数，GA表示计算的所述属性A的熵值增益。
  3. 根据权利要求1所述的方法，其特征在于，判断所述子属性是否满足成为所述核保决策树的叶子节点的预设条件的步骤，包括：
    当划分的子属性只有一个时，将所述子属性确定为所述核保决策树的叶子节点；或
    当划分的子属性的核保结果均为通过或者不通过时，将所述子属性确定为所述核保决策树的叶子节点；或
    当所述子属性的熵值增益小于预设的阈值时，将所述子属性确定为所述核保决策树的叶子节点。
  4. 根据权利要求1所述的方法，其特征在于，在所述将所述样本训练子集确定为所述样本训练集，对所述子属性递归计算所述熵值增益及划分操作，直到划分的下一节点的子属性满足成为所述核保决策树的叶子节点的预设条件之后，还包括：
    显示所述核保决策树并在所述核保决策树的叶子节点中显示对应属性的核保结果。
  5. 根据权利要求1至4任一项所述的方法，其特征在于，所述属性包括以下情况中的至少两种：年龄、行业风险、既往病史及赔付率。
  6. 一种核保决策树的创建装置，其特征在于，所述装置包括：
    样本获取模块，用于获取包括不同样本属性的样本训练集；
    熵值增益计算模块，用于根据所述样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益；
    节点划分模块，用于将熵值增益最高的属性作为核保决策树的当前节点，并将所述熵值增益最高的属性对应的子属性划分为所述当前节点的下一节点；
    子集抽取模块，用于从所述样本训练集中抽取划分的所述子属性的样本训练子集；及
    递归模块，用于将所述样本训练子集确定为所述样本训练集，对所述子属性递归计算所述熵值增益及划分操作，直到划分的下一节点的子属性满足成为所述核保决策树的叶子节点的预设条件。
  7. 根据权利要求6所述的装置，其特征在于，所述熵值增益计算模块通过以下公式计算所述熵值增益：
    Figure PCTCN2017104598-appb-100002
    其中，M表示样本训练集中总核保通过率，Ai表示对应属性A的子属性i的数量占样本训练集中的总数量的比例，Bi表示所述子属性i以所述属性A的数量为基数的核保通过率，n表示对应属性A的子属性的个数，GA表示计算的所述属性A的熵值增益。
  8. 根据权利要求6或7所述的装置，其特征在于，所述递归模块还包括：
    第一叶子节点确定单元，用于当划分的子属性只有一个时，将所述子属性确定为所述核保决策树的叶子节点；或
    第二叶子节点确定单元，用于当划分的子属性的核保结果均为通过或者不通过时，将所述子属性确定为所述核保决策树的叶子节点；或
    第三叶子节点确定单元，用于当所述子属性的熵值增益小于预设的阈值时，将所述子属性确定为所述核保决策树的叶子节点。
  9. 根据权利要求6所述的装置，其特征在于，所述装置还包括：
    显示模块，用于显示所述核保决策树并在所述核保决策树的叶子节点中显示对应属性的核保结果。
  10. 根据权利要求6至9任一项所述的装置，其特征在于，所述属性包括以下情况中的至少两种：年龄、行业风险、既往病史及赔付率。
  11. 一种计算机设备，包括存储器和一个或多个处理器，所述存储器中存储有计算机可读指令，所述计算机可读指令被所述处理器执行时，使得所述一个或多个处理器执行以下步骤：
    获取包括不同样本属性的样本训练集；
    根据所述样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益；
    将熵值增益最高的属性作为核保决策树的当前节点，并将所述熵值增益最高的属性对应的子属性划分为所述当前节点的下一节点；
    从所述样本训练集中抽取划分的所述子属性的样本训练子集；及
    将所述样本训练子集确定为所述样本训练集，对所述子属性递归计算所述熵值增益及划分操作，直到划分的下一节点的子属性满足成为所述核保决策树的叶子节点的预设条件。
  12. 根据权利要求11所述的计算机设备，其特征在于，所述处理器所执行的根据所述样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益的步骤，包括：
    通过以下公式计算所述熵值增益：
    Figure PCTCN2017104598-appb-100003
    其中，M表示样本训练集中总核保通过率，Ai表示对应属性A的子属性i的数量占样本训练集中的总数量的比例，Bi表示所述子属性i以所述属性A的数量为基数的核保通过率，n表示对应属性A的子属性的个数，GA表示计算的所述属性A的熵值增益。
  13. 根据权利要求11所述的计算机设备，其特征在于，所述处理器所执行的判断所述子属性是否满足成为所述核保决策树的叶子节点的预设条件的步骤，包括：
    当划分的子属性只有一个时，将所述子属性确定为所述核保决策树的叶子节点；或
    当划分的子属性的核保结果均为通过或者不通过时，将所述子属性确定为所述核保决策树的叶子节点；或
    当所述子属性的熵值增益小于预设的阈值时，将所述子属性确定为所述核保决策树的叶子节点。
  14. 根据权利要求11所述的计算机设备，其特征在于，在所述处理器所执行的将所述样本训练子集确定为所述样本训练集，对所述子属性递归计算所述熵值增益及划分操作，直到划分的下一节点的子属性满足成为所述核保决策树的叶子节点的预设条件的步骤之后，所述处理器执行所述计算机可读指令还用于实现以下步骤：
    显示所述核保决策树并在所述核保决策树的叶子节点中显示对应属性的核保结果。
  15. 根据权利要求11至14任一项所述的计算机设备，其特征在于，所述属性包括以下情况中的至少两种：年龄、行业风险、既往病史及赔付率。
  16. 一个或多个存储有计算机可读指令的非易失性可读存储介质，所述计算机可读指令被一个或多个处理器执行时，使得所述一个或多个处理器执行以下步骤：
    获取包括不同样本属性的样本训练集；
    根据所述样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益；
    将熵值增益最高的属性作为核保决策树的当前节点，并将所述熵值增益 最高的属性对应的子属性划分为所述当前节点的下一节点；
    从所述样本训练集中抽取划分的所述子属性的样本训练子集；及
    将所述样本训练子集确定为所述样本训练集，对所述子属性递归计算所述熵值增益及划分操作，直到划分的下一节点的子属性满足成为所述核保决策树的叶子节点的预设条件。
  17. 根据权利要求16所述的存储介质，其特征在于，所述处理器所执行的根据所述样本训练集中各属性的样本的核保结果，计算各属性中表示影响核保结果的熵值增益的步骤，包括：
    通过以下公式计算所述熵值增益：
    Figure PCTCN2017104598-appb-100004
    其中，M表示样本训练集中总核保通过率，Ai表示对应属性A的子属性i的数量占样本训练集中的总数量的比例，Bi表示所述子属性i以所述属性A的数量为基数的核保通过率，n表示对应属性A的子属性的个数，GA表示计算的所述属性A的熵值增益。
  18. 根据权利要求16所述的存储介质，其特征在于，所述处理器所执行的判断所述子属性是否满足成为所述核保决策树的叶子节点的预设条件的步骤，包括：
    当划分的子属性只有一个时，将所述子属性确定为所述核保决策树的叶子节点；或
    当划分的子属性的核保结果均为通过或者不通过时，将所述子属性确定为所述核保决策树的叶子节点；或
    当所述子属性的熵值增益小于预设的阈值时，将所述子属性确定为所述核保决策树的叶子节点。
  19. 根据权利要求16所述的存储介质，其特征在于，在所述处理器所执行的将所述样本训练子集确定为所述样本训练集，对所述子属性递归计算所述熵值增益及划分操作，直到划分的下一节点的子属性满足成为所述核保决策树的叶子节点的预设条件的步骤之后，所述处理器执行所述计算机可读指令还用于实现以下步骤：
    显示所述核保决策树并在所述核保决策树的叶子节点中显示对应属性的核保结果。
  20. 根据权利要求16至19任一项所述的存储介质，其特征在于，所述属性包括以下情况中的至少两种：年龄、行业风险、既往病史及赔付率。
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