WO2015097773A1 - 要因抽出システム、要因抽出方法 - Google Patents
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- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/043—Architecture, e.g. interconnection topology based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G06—COMPUTING; CALCULATING OR 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
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- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
Definitions
- This relates to technology for extracting explanatory variables that contribute to objective variables from event data describing event components.
- Patent Document 1 an explanatory variable that effectively contributes to the objective variable is specified by calculating the contribution of the explanatory variable to the objective variable.
- MR multiple regression analysis
- PLS partial least square regression analysis
- the explanatory variable is usually extracted from a data table that stores event data describing event components and their element values. However, it is not always the variable itself stored in the table, but a variable created by performing some processing on the variable stored in the table may be used as a new explanatory variable. Thereby, for example, a new explanatory variable having a temporal / spatial scale different from that of the original variable can be automatically generated, and a factor that facilitates decision making from various viewpoints can be extracted without a burden on the user.
- a new variable is created according to a predetermined rule / aggregation method based on a variable accumulated in a table, and is newly added as an explanatory variable.
- the rule / aggregation method there is an aggregation operation that averages every hour if there is a variable representing a time series.
- an explanatory variable that effectively contributes to the objective variable is specified by calculating a contribution degree of the explanatory variable to the objective variable.
- Non-Patent Document 1 describes a learning method described later in relation to the present invention.
- a typical analysis method example of the former is a single regression analysis
- a typical analysis method example of the latter is a multiple regression analysis, a principal component regression analysis, or a partial least square regression analysis.
- an explanatory variable having a large coefficient such as a, p1,..., PN is an explanatory variable that effectively contributes to the objective variable.
- the present invention has been made to solve the above-described problems, and efficiently identifies a combination of other variables that contribute to the fluctuation of the objective variable and extracts it as an explanatory variable (factor). Objective.
- the factor extraction system defines a covariant composite variable composed of a combination of event variables and a binary number indicating whether or not the combination exists in event data.
- a contributing factor is extracted by obtaining a correlation with a variable.
- the factor extraction system can automatically specify a combination of a plurality of explanatory variables that effectively contribute to an objective variable.
- FIG. 1 is a configuration diagram of a factor extraction system 300 according to Embodiment 1.
- FIG. It is a figure which shows the structure and data example of the event data table. It is a figure which shows the structure and example of data of the event variable table. It is a figure which shows the structure and data example of the covariant composite variable table 307. It is a figure which shows the structure and example of data of the objective variable table. It is a figure which shows the structure and data example of the contribution variable table 309. It is a figure which shows the example of the factor label.
- FIG. 3 is a detailed configuration diagram of an event variable conversion unit 303.
- FIG. It is a figure which illustrates the correspondence between the event data table 301 and the event variable table 304. It is another example which shows the correspondence between the event data table 301 and the event variable table 304. It is another example which shows the correspondence between the event data table 301 and the event variable table 304.
- 5 is a flowchart of processing in which an event variable conversion unit 303 converts an event data table 301 into an event variable table 304.
- 2 is a diagram illustrating a configuration of a covariant composite network 1301.
- FIG. 3 is a detailed configuration diagram of a covariant composite variable generation unit 305 and a covariant composite variable interpretation unit 306.
- FIG. An example is shown in which the key portion 3070 of the covariant composite variable table 307 is a character string representing a combination of coordinates.
- An example is shown in which the key portion 3070 is a character string combining people and things.
- the key unit 3070 is an example in which people and things are combined.
- It is a figure which shows the example of RBM. 12 is a flowchart of processing in which a covariant composite variable generation unit 305 converts an event variable table 304 into a covariant composite variable table 307.
- FIG. 6 is a flowchart for explaining processing of a covariant composite variable interpretation unit 306.
- 12 is a flowchart for describing processing of a contribution variable selection unit 308.
- 5 is a flowchart showing a processing flow of a factor label output unit 310.
- 10 is a detailed configuration diagram of a covariant composite variable generation unit 305 according to Embodiment 2.
- FIG. 1 is a diagram schematically showing a method of extracting explanatory variables contributing to an objective variable in the conventional method.
- explanatory variables contributing to the objective variable 101 are extracted from the explanatory variable group 102.
- the contribution degree 103 is a numerical value representing the strength of contribution of each explanatory variable to the objective variable 101.
- the degree of contribution 103 can be obtained by single regression analysis, multiple regression analysis, principal component regression analysis, partial least square regression analysis, or the like.
- X2, and x3 are represented by the respective coefficients a1, a2, and a3, and do not represent that the combination of the explanatory variables x1, x2, and x3 contributes to the objective variable. Please note that.
- FIG. 2 is a diagram for explaining the processing outline of the factor extraction system according to the present invention.
- the event data 202 is given to the system and an explanatory variable (contribution factor) that contributes to the specified objective variable 201 is extracted.
- the event data 202 is data describing the contents of the event.
- An event is a combination of one or more sets of elements such as “work start time” and “worker” and element values (numbers, character strings, codes (numeric strings handled as character strings)) of each element. It is composed.
- the event data 202 holds one or more events in a table format, for example.
- the system converts event data 202 into event variables 203.
- the system selects a covariant composite variable 204 that has a high contribution to the objective variable 201.
- the system uses the selected covariant compound variable 204 to output a factor label.
- the factor label here represents a combination of events represented by the covariant composite variable 204 as a character string so that the user can easily visually check the selected covariant composite variable 204.
- FIG. 3 is a configuration diagram of the factor extraction system 300 according to the first embodiment of the present invention.
- the factor extraction system 300 includes an event variable conversion unit 303, a covariant complex variable generation unit 305, a covariant complex variable interpretation unit 306, a contribution variable selection unit 308, and a factor label output unit 310. Further, as shown, each table shown in the figure is generated and stored in a storage device such as a hard disk.
- the factor extraction system 300 receives the event data table 301 and outputs a factor label 311.
- each component of the factor extraction system 300 will be described.
- FIG. 4 is a diagram showing a configuration of the event data table 301 and data examples.
- the event data table 301 has a key part 3010 and a value part 3011.
- the key part 3010 holds a character string representing the meaning of each field (column).
- a key part 3010 corresponds to a component of an event.
- the value part 3011 holds a character string, a numerical value, a sign, and the like indicating the value of each field of each record.
- the value part 3011 corresponds to the element value of the component.
- One line of the value part 3011 corresponds to one record (one event).
- the value unit 3011 can be configured by a flow line connecting a plurality of coordinate values.
- all coordinate values in the coordinate space are listed in the key portion 3010, and only the coordinate value that the operator has passed on the flow line is set to “1”, and the coordinate value that has not passed is “0”.
- the same flow line can be represented by “”.
- the latter is adopted, and the event data table 301 and the event variable table 304 have the same data structure for the “stay position” for convenience of processing.
- FIG. 5 is a diagram showing the configuration of the event variable table 304 and data examples.
- the event variable conversion unit 303 converts the event data table 301 into the event variable table 304. Details of this processing will be described later.
- the event variable table 304 has a key part 3040 and a value part 3041.
- the key part 3040 further includes an event name part 3042 and an event value part 3043.
- the event name part 3042 and the event value part 3043 are a list of combinations of the key part 3010 and the value part 3011 in the event table 301.
- the value part 3041 is a numerical value
- enumerating all the numerical values makes the number of event value parts 3043 enormous, so the value of the value part 3041 may be divided into an appropriate number of classes.
- the event name portion 3042 “work start time” is collected into three sections. This process will be described later.
- the value part 3041 holds “1” when an event represented by the combination of the event name part 3042 and the event value part 3043 is described in the event data table 301, and “0” when it is not described. Hold.
- the first record and the fourth record indicate that an event “worker is west” has occurred
- the second record and the fourth record indicate that “the worker is Hirayama. This indicates that the event “
- the event value part 3043 includes a plurality of flow lines representing the flow line. It becomes a coordinate value set, and the value portion 3041 indicates whether the flow line is described in the event data table 301 by “0” or “1”.
- FIG. 6 is a diagram illustrating a configuration of the covariant composite variable table 307 and a data example.
- the covariant composite variable generation unit 305 and the covariant composite variable interpretation unit 306 convert the event variable table 304 into a covariant composite variable table 307.
- a covariant composite variable is a variable newly introduced in the present invention, and whether or not a combination of one or more events represented by each record held in the event variable table 304 exists in the event data table 301. Is a variable that represents Details of the covariant compound variable will be described later.
- the covariant composite variable table 307 has a key part 3070 and a value part 3071.
- the key part 3070 holds a character string obtained by further combining one or more sets of the event name part 3042 and the event value part 3043 in the event variable table 304.
- the event represented by the key part 3070 (that is, a composite event obtained by combining one or more events represented by the set of the event name part 3042 and the event value part 3043 in the event variable table 304) is stored in the event data table 301. “1” is held if it is described in “1”, and “0” is held if it is not described.
- the contribution covariant selection unit 308 converts the covariant composite variable table 307 into the contribution variable table 309 using the objective variable table 302 as an input parameter. These tables are described below.
- FIG. 7 is a diagram showing a configuration of the objective variable table 302 and data examples.
- the objective variable table 302 is obtained by extracting what should be the objective variable (for example, a variable that the user is trying to optimize) from among the event components (fields) described in the event data table 301.
- the key part of this table holds a character string indicating the name of the target variable, and the value part holds the value of the variable.
- the value of the value part is a numerical value, and its form such as continuous value or discrete value is not limited.
- FIG. 8 is a diagram showing a configuration of the contribution variable table 309 and an example of data.
- the contribution variable selection unit 308 calculates the contribution degree of the covariant composite variable indicated by each column of the covariant composite variable table 307 to the objective variable (one in FIG. 7) indicated by each column of the objective variable table 302, and contributes. Only the covariant compound variable whose degree is equal to or greater than the threshold value is left and stored in the contribution variable table 309.
- a contribution degree a correlation coefficient, the multiple regression coefficient in multiple regression analysis, etc. can be used, for example. Any other suitable coefficient may be used as long as the degree of similarity between two sequences can be calculated.
- the contribution variable table 309 has a key part 3090 and a value part 3091.
- FIG. 9A is a diagram illustrating an example of the factor label 311.
- the factor label output unit 310 generates a factor label 311 from the key unit 3090 of the contribution variable table 309 and outputs it.
- the factor label 311 is a character string representing an event having a high contribution to the target variable 201, and is a character string held in the key part 3090 of the contribution variable table 309 and output for each column.
- the factor label character string 910 in FIG. 9A is output from the first column of the key part 3090, and the factor label character string 920 is output from the second column of the key part 3090.
- the factor label 311 can also be output in an image format, for example.
- the factor label image 921 is an image showing a movement route by connecting the coordinate values corresponding to the contents of the event value part 3043 in the factor label character string 920 with line segments.
- FIG. 9B is a diagram showing a correspondence relationship between the event variable table 304 and the objective variable table 302.
- the worker named West has a high working efficiency in the morning”.
- a record whose value part 3041 corresponding to these events is 1 in the event variable table 304 has a favorable numerical value in the objective variable table 302. That is, it can be said that the objective variable can be improved by adjusting the “work start time” as a control variable for the “worker”.
- FIG. 9C is a diagram for explaining the correspondence between the event variable table 304 and the factor label image 921.
- the event variable table 304 represents the worker's stay position coordinates, by displaying this as the factor label image 921, it can be easily understood that the combination of events represents the flow line of the worker.
- a global phenomenon called a flow line can be expressed by combining local events called stay positions, and this can be easily grasped by imaging.
- FIG. 10 is a detailed configuration diagram of the event variable conversion unit 303.
- the event variable conversion unit 303 includes a data reading unit 30306, a data dividing unit 30307, a value type determining unit 30308, a range dividing unit 30309, a distribution DB (DataBase) 30310, a range label adding unit 30311, a numerical value distributing unit 30312, and a column combining unit 30313.
- a distribution parameter input unit 30304 is connected to the distribution DB 30310, and a division parameter input unit 30305 is connected to the range dividing unit 309. These input units may be configured as a part of the event variable conversion unit 303.
- the data reading unit 30306 reads the event data table 301 and sends it to the data dividing unit 30307.
- the data dividing unit 30307 divides the event data into columns and sends the event data to the value type determining unit 30308.
- the value type determination unit 30308 determines whether the value of each column is a numerical value / character string / sign.
- the data determined to be a numerical value is sent to the range dividing unit 30309, and the data determined to be a character string or a code is sent to the pattern extracting unit 30314.
- an Arabic numeral or a symbol representing a numerical value (-(minus sign), + (plus sign), i (sign indicating imaginary number), decimal point, square root, etc.) is included, it is regarded as a numerical value and a character is included. Can be regarded as a sign.
- the event data and the distribution function are compared, and if it is close to the distribution function, it can be regarded as a numerical value.
- the range division unit 30309 refers to the distribution DB 30310 and divides the data received from the value type determination unit 30308 into range (class).
- the distribution DB 30310 stores parameters that represent typical distribution functions, that is, distribution shapes such as normal distribution, Laplace distribution, and logistic distribution. The user can input parameters of this distribution shape via the distribution parameter input unit 30304.
- the distribution parameter is an average value ⁇ and a variance value ⁇ in the case of a normal distribution. In the case of Poisson distribution, it is the expected number of occurrences ⁇ of events occurring in a predetermined section.
- a parameter for how many pieces of data are to be divided can be input via a division parameter input unit 30305.
- the number of sections is determined so that the number of event data included in each divided section is equal, (b) an average value and a variance value of event data are calculated, and based on the average value and the variance value (C) a value designated by the user is divided as a section break, and (d) a value range of event data is equally divided.
- the range label adding unit 30311 adds a range label to each divided section divided by the range dividing unit 30309. The procedure for adding a range label will be described later.
- the numerical value distribution unit 30312 allocates the event data determined as a numerical value by the value type determination unit 30308 to the corresponding range label, and sends it to the column combination unit 30313.
- the column combining unit 30313 combines the columns and stores them in the event variable table 304. The processing by the column combination unit 30313 is described in accordance with the example shown in FIG. 5.
- the event value unit 3043 “low” to “high” are combined to correspond to a single event name unit 3042. It corresponds to.
- the pattern extraction unit 30314 scans the event data in the row direction and extracts a character string / code pattern with the same notation.
- the character string label adding unit 30315 adds a character string label to the character string / code pattern extracted by the pattern extracting unit 30314. Processing for adding a character string label will be described later.
- the character string sorting unit 30316 associates the event data determined as the character string / code by the value type determination unit 30308 with the corresponding character string / code pattern, and sends the event data to the column combination unit 30313.
- the column combining unit 30313 combines the columns and stores them in the event variable table 304 as in the case of numerical values.
- FIG. 11A is a diagram illustrating a correspondence relationship between the event data table 301 and the event variable table 304.
- the value type determination unit 30308 determines as a numerical value is taken up.
- the key part 3010 of the event data table 301 indicates a component “temperature”.
- the value part 3011 of the record 1 is 16, the value part 3011 of the record 2 is 80, the value part 3011 of the record 3 is 50, and a total of M records are stored.
- the event value portion 3043 of the event variable data 304 includes “temperature L”, “temperature M”, and “temperature H”. Each record in the event variable table 304 corresponds to each record in the event data table 301.
- the event variable conversion unit 303 divides each value of the value part 3011 in each record in the event data table 301 into three value ranges, sets the value part 3041 corresponding to the value range to which each record belongs to 1, and sets the other value parts 3041 is set to 0.
- a procedure for dividing each value of the value part 3011 shown in FIG. 11A into a range will be described.
- the labels “temperature L”, “temperature M”, and “temperature H” of each section provided by the range label adding unit 30311 can also be instructed. Therefore, “16” belongs to “temperature L”, “80” belongs to “temperature H”, and “50” belongs to “temperature M”.
- FIG. 11B is another example showing a correspondence relationship between the event data table 301 and the event variable table 304.
- an example of data that the value type determination unit 30308 determines as a character string will be taken up.
- the key unit 3010 indicates a component “worker”.
- the value part 3011 included in this column is “Suzuki”, “Tanaka”, and “Suzuki”, all of which are composed of character strings.
- the pattern extraction unit 30314 removes duplicate elements and extracts two character string patterns “Suzuki” and “Tanaka”.
- the character string sorting unit 30316 sets the value part 3041 having the event value part 3043 corresponding to each value part 3011 in the event data table 301 to 1 and sets the other value parts 3041 to 0.
- FIG. 11C is another example showing a correspondence relationship between the event data table 301 and the event variable table 304.
- an example of data determined by the value type determination unit 30308 as a code is taken up.
- the same processing as in FIG. 11B is performed.
- FIG. 12 is a flowchart of processing in which the event variable conversion unit 303 converts the event data table 301 into the event variable table 304. Hereinafter, each step of FIG. 12 will be described.
- the data reading unit 30306 reads the event data table 301 (S1201).
- the data dividing unit 30307 divides event data into columns.
- the data dividing unit 30307 initializes a variable i for counting columns.
- Step S1204 The value type determination unit 30308 scans the i-th column of event data in the row direction, and determines whether all the elements included in the column are numerical values or are composed of character strings / codes. If all the elements are configured with numerical values, the process proceeds to step S1207. If all the elements are configured with character strings or codes, the process proceeds to step S1205.
- the pattern extraction unit 30314 removes duplicate elements from the value unit 3011 of each column and extracts a character string pattern (or code pattern) (S1205).
- the character string label adding unit 30315 generates a character string label based on the extraction result (S1206).
- FIG. 12 Steps S1207 to S1208
- the range dividing unit 30309 divides the value portion 3011 of each column into range (S1207)
- the range label adding unit 311 assigns a character string label to each section (S1208).
- Step S1209 The numerical value sorting unit 30312 (or the character string sorting unit 30316) increments the column number i by 1, and determines whether the value of i is equal to or smaller than the number N of columns in the event data table 301. If i is N or less (an unprocessed column remains), the process returns to step S1204. If all columns have been processed, the process proceeds to step S1210.
- the column combination unit 30313 sequentially combines the character string labels in the horizontal direction and stores them in the event variable table 304.
- the column combining unit 30313 stores the combined event variables in the event variable table 304.
- bonds a character string label in step S1210 was demonstrated, it combined this with an event variable at the same time as producing
- the set of column labels may be expanded sequentially in the column direction.
- FIG. 13A is a diagram showing a configuration of the covariant composite network 1301.
- the covariant composite variable generation unit 305 and the covariant composite variable interpretation unit 306 convert the event variable table 304 into the covariant composite variable table 307 using the covariant composite network 1301 illustrated in FIG.
- a covariant composite network 1301 and a conversion procedure using the same will be described with reference to FIG.
- the covariant composite network 1301 is a machine learning network in which a plurality of nodes are connected by weighted links, and has a covariant composite node group 1310 and an event node group 1320.
- the covariant composite node group 1310 has an arbitrary number of covariant composite nodes predetermined by the user.
- the event node group 1320 has the same number of event nodes as the number of columns in the event variable table 304.
- the covariant composite node and the event node are variable nodes each having a value of 0 or 1. Each record value of the event variable table 304 is input to the event node group 1320, and the covariant composite network 1301 uses this to perform machine learning as described below.
- Each covariant composite node is coupled to all event nodes via a composite link 1330, and each event node is coupled to all covariant composite nodes via a composite link 1330.
- the composite link 1330 has a composite weight value 1340 that indicates the strength of the connection between the covariant composite node and the event node.
- Each covariant composite node has a covariant composite node bias value 1312 indicating the likelihood of becoming 1 of the covariant composite node.
- Each event node has an event node bias value 1322 indicating the likelihood of becoming 1 of the event node.
- FIG. 13 shows the bias value only for the rightmost node.
- the covariant composite network 1301 calculates the value of the covariant composite node group 1310 from the value of the event node group 1320, and calculates the value of the event node group 1320 from the value of the covariant composite node group 1310. Has a mechanism. That is, when event variable data is input from each row of the event variable table 304 to the event node group 1320, the value of the event node group 1320, the composite weight 1340 and the covariant composite node bias 1312 that each event node has, The value of the covariant composite node group 1310 is determined by the calculation using, and each covariant composite node takes a value of 0 or 1.
- the value of the event node group 1320 is determined by the calculation using the value of the covariant composite node group 1310 and the composite weight 1340 and event node bias 1322 of each covariant composite node.
- the event node takes a value of 0 or 1.
- the covariant composite node connected to the event node or the combination of the plurality of event nodes is also set to 1. Converge. Since each column of the event variable table 304 indicates whether or not the event represented by the column exists in the event data table 301 (that is, whether or not the event has occurred), the value of the covariant composite node is 1. In this case, the events represented by the columns of the event variable table 304 connected thereto are simultaneously occurring. That is, the covariant composite node is 1 when there is a combination of events represented by the connected event nodes, and 0 when there is no combination.
- a covariant composite variable representing a combination of events can be generated by machine learning of the tendency of the event variable table 304 using the covariant composite network 1301.
- This covariant composite variable is preferably configured to represent a combination of events that frequently occur with each other.
- the composite weight 1340, the covariant composite node bias 1212, and the event node bias 1322 are obtained by machine learning under the condition that a covariant composite node is configured by a combination of a plurality of event nodes that frequently take a value of 1 from each other. It needs to be adjusted.
- a record of the event variable table 304 is input to the event node group 1320 using a mutual calculation mechanism between the covariant composite node group 1310 and the event node group 1320.
- the value of the covariant composite node group 1310 is calculated again, and the value of the event node group 1320 is recalculated again.
- the value of the input event node group 1320 and the event node group 1320 obtained as a result of the mutual calculation are calculated.
- a self-organizing machine learning method is generally well known. In the present embodiment, for example, a restricted Boltzmann machine is used.
- FIG. 13B is a diagram illustrating an example of the covariant composite network 1301 after learning.
- a composite link 1330 having a composite weight 1330 that is equal to or less than a certain threshold value is deleted by using the fact that a low value among the composite weights 1330 has a small influence on the behavior of the network 1301.
- the RBM is used for learning the covariant composite parameter, but the (0, 1) state of the event node group and the (0, 1) state of the covariant composite node are mutually unique.
- Other learning methods can be used as long as the parameters are learned so that the difference between the value of the input event node group 1320 and the value of the event node group 1320 obtained as a result of the mutual calculation becomes small.
- an auto encoder Auto Encoder
- FIG. 14 is a detailed configuration diagram of the covariant composite variable generation unit 305 and the covariant composite variable interpretation unit 306.
- the composite parameter DBs 3057 and 3062 are separated for convenience of description, but they may be shared between the covariant composite variable generation unit 305 and the covariant composite variable interpretation unit 306.
- the event value reading unit 3051 reads the value part 3041 of the event variable table 304 that is an input to the covariant composite variable generation unit 305.
- the composite parameter is calculated according to the RBM learning rule.
- the RBM learning rule will be described later.
- the input vector data y k is created by the number of rows in the table.
- the composite data conversion unit 3055 converts the value unit 3041 into the value unit 3071 of the covariant composite variable table 307 using the composite parameter DB 3057 and the vector data y k obtained from the input data conversion unit 3054.
- the conversion rule the RBM forward calculation rule is used.
- the RBM forward calculation rule will be described later.
- the covariant composite value writing unit 3056 stores the value part obtained by the conversion in the value part 3071 of the covariant composite variable table 307.
- the event key reading unit 3061 reads the key unit 3040 of the event variable table 304.
- the covariant composite key generation unit 3063 generates the key unit 3070 of the covariant composite variable table 307 using the read key unit 3040 and the composite parameter DB 3062.
- the key part 3070 is generated by concatenating the key part 3040 to be combined as a character string with a delimiter “&”. Which event variable combination the covariant compound variable is generated by can be determined using the RBM backward calculation rule. The RBM backward calculation rule will be described later.
- the covariant composite key image generation unit 3065 converts the character string of the key unit 3070 generated by the covariant composite key generation unit 3063 into an image that can be imaged. Hereinafter, this image is referred to as a key image. An example of the imaging process will be described later.
- the covariant composite key writing unit 3064 writes the generated key unit 3070 and key image to the key unit 3070 of the covariant composite variable table 307.
- FIG. 15A shows an example in which the key part 3070 of the covariant composite variable table 307 is a character string representing a combination of coordinates.
- the covariant composite key image generation unit 3065 generates a key image in which pixels corresponding to coordinates existing in the character string are filled. According to this example, for example, by displaying an image of a combination of position coordinates where the worker stayed, it can be easily understood that the combination of coordinates means an L-shaped flow line.
- FIG. 15B shows an example in which the key portion 3070 is a character string combining people and things.
- the covariant composite key image generation unit 3065 generates a key image in which the person and the object included in the key unit 3070 are plotted using the attributes of the person and the object as axes of the graph.
- the key unit 3070 means a group of workers with long working years by using the working years and working start times, which are the attributes of the workers, as axes of the graph. .
- FIG. 15C is an example in which the key unit 3070 combines people and things.
- the covariant composite key image generation unit 3065 generates a key image in which the person or thing included in the key unit 3070 is plotted on the map when the map information of the position of the person or thing exists.
- the key portion 3070 means a seat group in the center of the area by using the seating chart as map information.
- FIG. 16 is a diagram illustrating an example of the RBM.
- the values of the visible layer element v and the hidden layer element h can be calculated by the following equations (1) and (2).
- Equation (1) is the RBM backward calculation rule
- Equation (2) is the RBM forward calculation rule
- the weighting coefficient matrix W, the hidden layer bias b, and the visible layer bias c, which are parameters, are repeatedly calculated by giving the learning vector data x k to the visible layer element v according to the following equations (4) to (6). Is required.
- ⁇ is a learning coefficient.
- the update amounts ⁇ W ij , ⁇ b i , ⁇ c j are obtained by the following equations (7) to (9).
- Equation (4) to (9) are RBM learning rules.
- FIG. 17 is a flowchart of processing in which the covariant composite variable generation unit 305 converts the event variable table 304 into the covariant composite variable table 307.
- the covariant composite variable generation unit 305 converts the event variable table 304 into the covariant composite variable table 307.
- each step of FIG. 17 will be described.
- the event value reading unit 3051 reads the value unit 3041 of the event variable table 304 (S1701).
- Learning data conversion unit 3052 the value in the read table is converted into vector data x k for a composite parameter learning.
- the input data conversion unit 3054 converts the value unit 3041 into vector data for conversion into the value unit 3071 of the covariant composite variable table 307. Specifically, input vector data yk is created.
- the covariant composite value writing unit 3056 writes the covariant composite vector data d k obtained in step S1705 into the value unit 3071 of the covariant composite variable table 307. Specifically, d k is written in the k-th row of the value portion 3071. At this time, the value of the k-th row and the j-th column is defined as d kj .
- FIG. 18 is a flowchart for explaining the processing of the covariant composite variable interpretation unit 306. Hereinafter, each step of FIG. 18 will be described.
- the event key reading unit 3061 reads the key unit 3040 from the event variable table 304.
- the covariant composite key generation unit 3063 generates the key unit 3070 of the covariant composite variable table 307.
- the key part 3070 is generated by character string concatenating the key part 3040 of the event variable group constituting the covariant composite variable. Whether or not a covariant composite variable is configured by which event variable group can be determined using RBM's backward calculation rule (1).
- Step S1802 Supplement
- a vector in which only the j-th element is set to “1” and the others are set to “0” is given to the hidden layer element h that is an input of the expression (1).
- the event variable group corresponding to the element “1” in the visible layer element v obtained as a result is the event variable constituting the covariant composite variable.
- the covariant composite key image generation unit 3065 generates a key image for the generated key unit 3070 that can be imaged (S1803).
- the covariant composite key writing unit 3064 stores the generated key unit 3070 and key image in the key unit 3070 of the covariant composite variable table 307. Since the key image is intended to make it easier for the user to visually grasp the key portion 3070, for example, the key image may be generated when the user explicitly instructs.
- FIG. 19 is a flowchart for explaining the processing of the contribution variable selection unit 308.
- the contribution variable selection unit 308 reads the covariant composite variable table 307 (S1901) and the objective variable table (S1902).
- the contribution variable selection unit 308 calculates the contribution degree of each numerical value of the value part 3071 of the covariant composite variable table 307 to the numerical value of the value part of the objective variable table 302 (S1903).
- the contribution variable selection unit 308 stores, in the contribution variable table 309, only those that have obtained a contribution degree equal to or greater than the threshold among the columns of the covariant composite variable table 307 (S1904).
- FIG. 20 is a flowchart showing the processing flow of the factor label output unit 310.
- the factor label output unit 310 reads the contribution variable table 309 (S2001).
- the factor label output unit 310 outputs all the contribution variable table keys stored in the key part of the read contribution variable table 309 as the factor label 311 (S2002).
- the factor extraction system 300 converts the event data table 301 into an event variable table 304 and further converts it into a covariant composite variable table 307.
- a combination of a plurality of events that effectively contribute to the target variable can be automatically specified. That is, it is not necessary to manually create a combination of events and obtain the contribution, so that it is possible to efficiently identify the contributing factors and to make a decision for improving the objective variable.
- the factor extraction system 300 obtains a contribution degree to the objective variable for each combination of constituent elements of event data (that is, individual events) by converting the event data table 301 to the event variable table 304. Can do.
- Patent Documents 1 to 3 for example, even if an explanatory variable contributing to the objective variable is found, only a regression relationship between the continuous value and the continuous value between the objective variable and the explanatory variable is found. It is difficult for the user to understand what action should be taken to actually change the objective variable.
- the explanatory variable 104 “number of conveyances” contributing to the objective variable 101 is extracted. How can the objective variable 101 be adjusted by changing this “number of conveyances”? It cannot be easily understood.
- FIGS. 9A to 9C it is possible to easily understand what kind of event or action combination contributes to the objective variable. .
- FIG. 21 is a detailed configuration diagram of the covariant composite variable generation unit 305 according to the second embodiment.
- the user inputs parameters to be described later via the parameter input unit 3058, and the composite parameter learning unit 3053 learns composite parameters using this.
- Other configurations are the same as those of the first embodiment. Accordingly, the other steps in FIG. 17 are the same as those in the first embodiment except for the operation in step S1703.
- the composite parameter learning unit 3053 learns composite parameters so that the number of event node groups 1320 connected to the covariant composite node group 1310 is effectively reduced. In order to realize this, the number of visible layer nodes v i that become “1” when “1” is given to a certain hidden layer node h j in the RBM backward calculation rule shown in Equation (1). What is necessary is just to learn a composite parameter so that it may decrease. That is, the iterative calculation may be advanced after adjusting the value of the parameter W ij or c i so as to reduce the output value of Equation (1).
- the composite parameter learning unit 3053 performs iterative calculation exemplified by the following equation (10) in parallel with, for example, equations (4) to (6) of the RBM learning rule. For each iterative calculation, the calculations of equations (4) to (6) are performed once and the calculation of equation (10) is performed once.
- P in the last term of Expression (10) is a parameter, which can be specified by the user via the parameter input unit 3058.
- Expression (10) for example, a target average value of the number of event nodes connected to the covariant composite node is designated.
- P (v i 1
- h ) is the probability of a visible layer element v i becomes 1 when the hidden layer is h.
- the RBM is learned so as to correct the probability that the visible layer becomes 1 when the value of the hidden layer is given. That is, when performing the mutual calculation in the mutual calculation mechanism of the covariant composite network 1301, when a certain covariant composite node becomes “1”, the number of event nodes that become “1” decreases along with this. The composite parameters of the covariant composite network 1301 will be calculated.
- Equation (10) reduces the output value of Equation (1) by reducing the value of c i , thereby reducing the number of event node groups 1320 connected to the covariant composite node group 1310.
- Other calculation formulas may be used as long as the value of W ij or c i can be adjusted so that the number of visible layers having a value of “1” in formula (1) is reduced.
- a covariant composite node can be configured by a combination of fewer event nodes. As a result, the number of event variables constituting the covariant composite variable is reduced, and the factor label 311 that is easy for the user to make a decision can be output.
- the present invention is not limited to the embodiment described above, and includes various modifications.
- the above embodiment has been described in detail for easy understanding of the present invention, and is not necessarily limited to the one having all the configurations described.
- a part of the configuration of one embodiment can be replaced with the configuration of another embodiment.
- the configuration of another embodiment can be added to the configuration of a certain embodiment. Further, with respect to a part of the configuration of each embodiment, another configuration can be added, deleted, or replaced.
- the above components, functions, processing units, processing means, etc. may be realized in hardware by designing some or all of them, for example, with an integrated circuit.
- Each of the above-described configurations, functions, and the like may be realized by software by interpreting and executing a program that realizes each function by the processor.
- Information such as programs, tables, and files for realizing each function can be stored in a recording device such as a memory, a hard disk, an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, or a DVD.
- 300 factor extraction system
- 301 event data table
- 302 objective variable table
- 303 event variable conversion unit
- 304 event variable table
- 305 covariant complex variable generation unit
- 306 covariant complex variable interpretation unit
- 307 Covariant composite variable table
- 308 contribution variable selection unit
- 309 contribution variable table
- 310 factor label output unit
- 311 factor label.
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Abstract
Description
以下では本発明の課題に対する理解を促進するため、まず説明変数の組み合わせ総数について説明し、その後に本発明の実施形態について説明する。
図2は、本発明に係る要因抽出システムの処理概要を説明する図である。ここでは、イベントデータ202がシステムに対して与えられ、指定した目的変数201に寄与する説明変数(寄与要因)を抽出することを想定する。
データ読込部30306は、イベントデータテーブル301を読み込む(S1201)。データ読込部30306は、変換結果を格納する変数をクリア、すなわち行数=0、列数=0とする(S1202)。
データ分割部30307は、イベントデータを列単位に分割する。データ分割部30307は、分割した列の数を変数Nに代入し、1つの列の長さ(=イベントデータの個数=レコード数)を変数Mに代入する。データ分割部30307は、列をカウントするための変数iを初期化する。
値タイプ判定部30308は、イベントデータのi番目の列を行方向に走査し、当該列に含まれる要素が全て数値であるか、または、文字列/符号で構成されるかを判定する。全ての要素が数値で構成される場合はステップS1207へ進み、文字列または符号で構成される場合はステップS1205へ進む。
パターン抽出部30314は、各列のバリュー部3011から重複要素を取り除いて文字列パターン(または符号パターン)を抽出する(S1205)。文字列ラベル追加部30315は抽出結果に基づき文字列ラベルを生成する(S1206)。
図11Aで例示した手法にしたがって、値域分割部30309は各列のバリュー部3011を値域に分割し(S1207)、値域ラベル追加部311は各区間に文字列ラベルを割り当てる(S1208)。
数値振り分け部30312(または文字列振り分け部30316)は、列番号iを1つ増やし、iの値がイベントデータテーブル301の列数N以下であるか否かを判定する。iがN以下である(未処理の列が残っている)場合はステップS1204に戻り、全列について処理済であればステップS1210へ進む。
列結合部30313は、文字列ラベルを横方向に順次結合し、事象変数テーブル304に格納する。列結合部30313は、結合した事象変数を事象変数テーブル304に格納する。なお、ステップS1206とS1208において文字列ラベルを生成し、ステップS1210において文字列ラベルを結合する処理手順を説明したが、文字列ラベルを生成すると同時にこれを事象変数と結合して、事象変数と文字列ラベルのセットを逐次的に列方向へ拡張してもよい。
P(hj=1|v)=σ(bj+Σi Wij vi)、j=1 to M (2)
σ(x)=1/(1+exp(-x)) (3)
bi=bi+ηΔbi (5)
cj=cj+ηΔcj (6)
Δbi=vi - v^ i (8)
Δcj= P(hj=1|v) - P(hj=1|v^) (9)
事象バリュー読込部3051は、事象変数テーブル304のバリュー部3041を読み込む(S1701)。学習用データ変換部3052は、読み込んだテーブル内の値を、複合パラメータ学習用のベクトルデータxkに変換する。
複合パラメータ学習部3053は、複合パラメータ(=複合重み1340、共変複合ノードバイアス1312、事象ノードバイアス1322)を計算する。具体的には、上記式(4)~(9)の入力である可視層素子vに対して学習用ベクトルデータxkを与えて、式(4)~(9)の反復計算によって複合パラメータW、b、cを得る。得られた複合パラメータは、複合パラメータDB3057に格納する。
入力用データ変換部3054は、バリュー部3041を共変複合変数テーブル307のバリュー部3071に変換するためのベクトルデータに変換する。具体的には、入力用ベクトルデータykが作成される。
複合データ変換部3055は、ステップS1705で作成された入力用ベクトルデータykを、共変複合ベクトルデータに変換する。具体的には、上記式(2)の入力である可視層素子vに対して入力用ベクトルデータykを与えることにより得られた隠れ層素子hの値を、共変複合ベクトルデータdk=(dk1,・・・dkM)(k=1~R)として受け取る。
共変複合バリュー書出部3056は、ステップS1705で得られた共変複合ベクトルデータdkを、共変複合変数テーブル307のバリュー部3071に書き出す。具体的には、dkをバリュー部3071の第k行目に書き出す。このとき、第k行目の第j列目の値をdkjとする。
事象キー読込部3061は、事象変数テーブル304からキー部3040を読み込む。
共変複合キー生成部3063は、共変複合変数テーブル307のキー部3070を生成する。キー部3070は、当該共変複合変数を構成している事象変数群のキー部3040を文字列連結することによって生成される。ある共変複合変数が、どの事象変数群の組み合わせによって構成されているかについては、RBMの逆方向計算則の式(1)を用いて判定することができる。
共変複合変数テーブル307の第j列目の共変複合変数が、どの事象変数の組み合わせによって構成されているかを知りたいとする。このとき、式(1)の入力である隠れ層素子hに対して、j番目の要素のみを“1”とし、それ以外を“0”としたベクトルを与える。その結果得られた可視層素子vのうち“1”となっている要素に対応する事象変数群が、当該共変複合変数を構成している事象変数である。
共変複合キー画像生成部3065は、生成されたキー部3070のうち画像化できるものについて、キー画像を生成する(S1803)。共変複合キー書出部3064は、生成されたキー部3070およびキー画像を共変複合変数テーブル307のキー部3070に格納する。キー画像はユーザがキー部3070を視覚的に把握し易くするためのものであるため、例えばユーザが明示的に指示したときにキー画像を生成してもよい。
本実施形態1に係る要因抽出システム300は、イベントデータテーブル301を事象変数テーブル304に変換し、さらにこれを共変複合変数テーブル307に変換する。共変複合変数テーブル307を用いることにより、目的変数に対して効果的に寄与する複数の事象の組み合わせを自動的に特定することができる。すなわち、事象の組み合わせを手作業によって作成して寄与度を求める必要がなくなるので、寄与要因を効率的に特定し、目的変数を改善するための意思決定に役立てることができる。
実施形態1においては、事象変数の組み合わせによって共変複合変数を構成する例を説明した。これにより、事象の組み合わせを基準として目的変数を改善するための意思決定を支援することができる。ただし、目的変数に対して寄与する説明変数の組み合わせ個数があまりに多くなると(例えば数百、数千の事象の組み合わせ)、意思決定を却って妨げることになりかねない。そこで本発明の実施形態2においては、共変複合変数を構成する事象変数の組み合わせを効果的に削減する構成例について説明する。その他の構成は実施形態1と同様であるため、以下では上記と関連する共変複合変数生成部305について主に説明する。
本実施形態2に係る要因抽出システム300によれば、共変複合ノードをより少ない事象ノードの組み合わせによって構成することができる。これにより、共変複合変数を構成する事象変数の個数がより少なくなり、ユーザが意思決定しやすい要因ラベル311を出力することができる。
Claims (11)
- 目的変数に対して寄与する要因を抽出する要因抽出システムであって、
第1構成要素と第1要素値のセットが1以上組み合わさって構成されたイベントを複数記述したイベントデータを、前記第1構成要素と前記第1要素値の第1セットを第2構成要素とし、前記第2構成要素によって表されるイベントが前記イベントデータ内に存在するか否かを2値数で表した値を第2要素値とする事象変数を記述した事象変数データへ変換する、事象変数変換部、
前記事象変数データを、前記事象変数を構成する前記第2構成要素をさらに1以上組み合わせた第2セットを第3構成要素とし、前記第3構成要素によって表されるイベントが前記イベントデータ内に存在するか否かを2値数で表した値を第3要素値とする共変複合変数を記述した共変複合変数データへ変換する、共変複合変数変換部、
前記目的変数と前記共変複合変数との間の相関を求めることにより、前記目的変数に対する前記第3構成要素の寄与度を求め、その寄与度が所定閾値以上である前記第3構成要素を出力する寄与変数選択部、
を備えることを特徴とする要因抽出システム。 - 前記共変複合変数変換部は、前記第2構成要素と前記第3構成要素との間の相関関係を前記イベントデータに基づき機械学習し、前記第2構成要素に対する相関度が所定閾値以上である前記第3構成要素を特定することにより、前記第2構成要素と前記第2要素値のセットを前記第3構成要素と前記第3要素値のセットに変換する
ことを特徴とする請求項1記載の要因抽出システム。 - 前記事象変数変換部は、前記イベントデータが記述している全ての前記イベントについて前記第1構成要素と前記第1要素値を取得することにより、前記イベントデータ内に存在する前記第1構成要素と前記第1要素値との組み合わせを全て抽出し、その抽出した全ての組み合わせを前記第2構成要素として前記事象変数を生成する
ことを特徴とする請求項1記載の要因抽出システム。 - 前記事象変数変換部は、
前記イベントデータが記述している前記第1要素値が数値である場合は、前記数値に対応する前記第1構成要素を複数の数値範囲に分割することにより、前記イベントデータを前記第1構成要素と前記数値範囲との組み合わせに変換し、
さらに前記数値が前記複数の数値範囲のいずれに含まれるかを判定することにより、前記数値を前記第2構成要素と前記第2要素値の組み合わせに変換する
ことを特徴とする請求項3記載の要因抽出システム。 - 前記事象変数変換部は、
前記イベントデータが記述している前記第1要素値が文字列である場合は、前記イベントデータ内に存在する前記文字列を全て抽出することにより、前記イベントデータを前記第1構成要素と前記文字列との組み合わせに変換し、
さらに前記第1構成要素と前記文字列との組み合わせが前記イベントデータ内に含まれるか否かを判定することにより、前記文字列を前記第2構成要素と前記第2要素値の組み合わせに変換する
ことを特徴とする請求項3記載の要因抽出システム。 - 前記共変複合変数変換部は、前記事象変数データが記述している前記第2構成要素が、1つの前記イベント内における被測定物の移動軌跡を表している場合は、前記事象変数データが記述している全ての前記移動軌跡を前記第3構成要素として採用する
ことを特徴とする請求項1記載の要因抽出システム。 - 前記要因抽出システムはさらに、前記寄与変数選択部が選択した前記第3構成要素を出力する要因ラベル出力部を備える
ことを特徴とする請求項1記載の要因抽出システム。 - 前記要因抽出システムはさらに、前記寄与変数選択部が選択した前記第3構成要素を出力する要因ラベル出力部を備え、
前記要因ラベル出力部は、前記第3構成要素が前記移動軌跡を表している場合は、その移動軌跡を画像化したものを前記第3構成要素として出力する
ことを特徴とする請求項6記載の要因抽出システム。 - 前記共変複合変数変換部は、1以上の前記第2構成要素と前記第3構成要素との間の第1結合度を、前記前記第2構成要素から前記第3構成要素を学習する過程において機械学習するとともに、前記第3構成要素から前記第2構成要素を逆学習する過程において前記第3構成要素と前記第2構成要素との間の第2結合度を機械学習することを繰り返すことにより、前記事象変数データを前記共変複合変数データへ変換する
ことを特徴とする請求項1記載の要因抽出システム。 - 前記共変複合変数変換部は、前記第2結合度を機械学習する過程において、前記第2結合度の値を所定規則にしたがって減算することにより、前記減算を実施しない場合と比較して、前記第3構成要素と結合する前記第2構成要素の個数を削減する
ことを特徴とする請求項9記載の要因抽出システム。 - 目的変数に対して寄与する要因を抽出する要因抽出方法であって、
第1構成要素と第1要素値のセットが1以上組み合わさって構成されたイベントを複数記述したイベントデータを、前記第1構成要素と前記第1要素値の第1セットを第2構成要素とし、前記第2構成要素によって表されるイベントが前記イベントデータ内に存在するか否かを2値数で表した値を第2要素値とする事象変数を記述した事象変数データへ変換する、事象変数変換ステップ、
前記事象変数データを、前記事象変数を構成する前記第2構成要素をさらに1以上組み合わせた第2セットを第3構成要素とし、前記第3構成要素によって表されるイベントが前記イベントデータ内に存在するか否かを2値数で表した値を第3要素値とする共変複合変数を記述した共変複合変数データへ変換する、共変複合変数変換ステップ、
前記目的変数と前記共変複合変数との間の相関を求めることにより、前記目的変数に対する前記第3構成要素の寄与度を求め、その寄与度が所定閾値以上である前記第3構成要素を出力する寄与変数選択ステップ、
を有することを特徴とする要因抽出方法。
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