CN110874608A - Classification method and system and electronic equipment - Google Patents
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Abstract
The present disclosure provides a classification method, including obtaining an object to be classified, determining whether the object to be classified belongs to an object prone to misjudgment based on a first predetermined rule, and classifying the object to be classified based on a second predetermined rule when the object to be classified belongs to the object prone to misjudgment. The present disclosure also provides a classification system, an electronic device, and a computer-readable medium.
Description
Technical Field
The present disclosure relates to the field of internet technologies, and in particular, to a classification method, a classification system, and an electronic device.
Background
With the development of artificial intelligence, machine learning algorithms are increasingly applied to various fields. Classification algorithms are continuously studied by people as the basic and core algorithms for artificial intelligence. However, various classification models including naive bayes, decision trees, support vector machines, etc. have difficulty in achieving satisfactory classification results.
Disclosure of Invention
In view of the above, the present disclosure provides a classification method, system and electronic device.
One aspect of the present disclosure provides a classification method, including obtaining an object to be classified, determining whether the object to be classified belongs to an object prone to misjudgment based on a first predetermined rule, and classifying the object to be classified based on a second predetermined rule in a case where the object to be classified belongs to the object prone to misjudgment.
According to the embodiment of the present disclosure, the method further includes classifying the object to be classified through the trained classification model in a case that the object to be classified does not belong to an object that is prone to misjudgment.
According to the embodiment of the present disclosure, the determining, based on the first predetermined rule, whether the object to be classified belongs to an object prone to misjudgment includes determining, based on a set prone to misjudgment, whether the object to be classified is similar to an object in the set prone to misjudgment, and determining, when the object to be classified is similar to an object in the set prone to misjudgment, that the object to be classified belongs to an object prone to misjudgment.
According to an embodiment of the present disclosure, in the case that the object to be classified belongs to an object that is prone to be misjudged, classifying the object to be classified based on a second predetermined rule includes, in the case that the object to be classified is similar to an object in the set that is prone to be misjudged, classifying the object to be classified based on a classification result of the object in the set that is prone to be misjudged.
According to the embodiment of the present disclosure, the determining whether the object to be classified is similar to the objects in the erroneous judgment prone set based on the erroneous judgment prone set includes determining whether each attribute of the object to be classified is similar to each attribute of the objects in the erroneous judgment prone set, determining a number ratio of the attributes of the object to be classified that are similar to the objects in the erroneous judgment prone set, and determining whether the object to be classified is similar to the objects in the erroneous judgment prone set by comparing the ratio with a preset threshold.
According to the embodiment of the disclosure, the object to be classified is network flow data, and the obtaining of the object to be classified includes obtaining transport layer data of a network and processing the transport layer data to obtain network flow data.
Another aspect of the disclosure provides a classification system that includes an obtaining module, a first classification module, and a second classification module. And the obtaining module is used for obtaining the object to be classified. And the first classification module is used for determining whether the object to be classified belongs to the object which is easy to misjudge or not based on a first preset rule. And the second classification module is used for classifying the object to be classified based on a second preset rule under the condition that the object to be classified belongs to an object which is easy to misjudge.
According to the embodiment of the present disclosure, the system further includes a third classification module, configured to classify the object to be classified through the trained classification model when the object to be classified does not belong to an object that is prone to misjudgment.
According to the embodiment of the disclosure, the first classification module comprises a similarity judgment sub-module and a first classification sub-module. And the similarity judgment submodule is used for determining whether the object to be classified is similar to the object in the misjudgment-prone set or not based on the misjudgment-prone set. And the first classification submodule is used for determining that the object to be classified belongs to the object which is easy to misjudge under the condition that the object to be classified is similar to the object in the set which is easy to misjudge.
According to the embodiment of the present disclosure, the second classification module includes a second classification sub-module, configured to classify the object to be classified based on the classification result of the objects in the misjudgment prone set when the object to be classified is similar to the objects in the misjudgment prone set.
According to the embodiment of the disclosure, the object to be classified has a plurality of attributes, and the similarity judgment submodule includes an attribute comparison unit, a proportion determination unit, and a similarity judgment unit. And the attribute comparison unit is used for respectively determining whether each attribute of the object to be classified is similar to each attribute of the object in the misjudgment-prone set. And the proportion determining unit is used for determining the proportion of the number of the attributes of the objects to be classified, which are similar to the objects in the misjudgment set. And the similarity judgment unit is used for comparing the ratio with a preset threshold value to determine whether the object to be classified is similar to the object in the misjudgment-prone set.
According to the embodiment of the disclosure, the object to be classified is network flow data, and the obtaining module comprises a obtaining submodule and a processing submodule. And the obtaining submodule is used for obtaining the data of the transmission layer of the network. And the processing submodule is used for processing the data of the transmission layer to obtain network flow data.
Another aspect of the disclosure provides an electronic device comprising at least one processor and at least one memory storing one or more computer-readable instructions, wherein the one or more computer-readable instructions, when executed by the at least one processor, cause the processor to perform the method as described above.
Another aspect of the disclosure provides a computer readable medium having stored thereon computer readable instructions that, when executed, cause a processor to perform the method as described above.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
According to the method, whether the object to be classified belongs to the object prone to misjudgment is judged in advance, if the object to be classified belongs to the object prone to misjudgment, the classification result is determined directly based on the second preset rule and does not pass through the classification model any more, the situation of wrong classification is reduced to a great extent, and the classification accuracy is improved.
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The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:
fig. 1 schematically shows a schematic diagram of a classification method according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a classification method according to an embodiment of the present disclosure;
fig. 3A and 3B schematically illustrate a flow chart for determining whether the object to be classified is similar to an object in a misjudgment-prone set based on the misjudgment-prone set according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a block diagram of a classification system according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of a similarity determination sub-module in accordance with an embodiment of the present disclosure; and
FIG. 6 schematically illustrates a block diagram of a computer system suitable for implementing the classification method and system according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "a or B" should be understood to include the possibility of "a" or "B", or "a and B".
The embodiment of the disclosure provides a classification method, which includes obtaining an object to be classified, determining whether the object to be classified belongs to an object prone to misjudgment based on a first predetermined rule, and classifying the object to be classified based on a second predetermined rule under the condition that the object to be classified belongs to the object prone to misjudgment.
Fig. 1 schematically shows a schematic diagram of a classification method according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example to which the embodiments of the present disclosure can be applied to help those skilled in the art understand the technical content of the present disclosure.
According to the method of the embodiment of the disclosure, before the classification model is used for classifying the objects to be classified, the objects to be classified are firstly subjected to primary classification, and the objects which are easy to misjudge are screened from the objects to be classified and are separately processed. As shown in fig. 1, after the object to be classified is obtained, it is classified into a misjudgment-prone object and other objects. For other objects, directly inputting the classification model for processing to obtain a classification result; and for the object which is easy to misjudge, classifying the object according to other predetermined rules without using a classification model to obtain a classification result. In this way, the accuracy of the classification result can be significantly improved.
Fig. 2 schematically shows a flow chart of a classification method according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes operations S210 to S230.
In operation S210, an object to be classified is obtained.
According to the embodiment of the disclosure, the object to be classified may be network flow data, for example, and the obtaining the object to be classified includes obtaining transport layer data of a network and processing the transport layer data to obtain network flow data. The network flow data has various attributes, so the method is particularly suitable for applying the method disclosed by the embodiment of the disclosure, and can effectively improve the classification accuracy. It should be appreciated that the methods of the embodiments of the present disclosure can be applied to the classification process of various objects.
In operation S220, it is determined whether the object to be classified belongs to an object that is misjudged based on a first predetermined rule.
According to the embodiment of the present disclosure, the determining, based on the first predetermined rule, whether the object to be classified belongs to an object prone to misjudgment includes determining, based on a set prone to misjudgment, whether the object to be classified is similar to an object in the set prone to misjudgment, and determining, when the object to be classified is similar to an object in the set prone to misjudgment, that the object to be classified belongs to an object prone to misjudgment.
For example, historical classification results for a certain number of objects may be processed, and the historical classification results of the classification model used may be analyzed to distinguish between objects that are classified correctly and objects that are classified incorrectly. And adding the object with the wrong classification or the object with the classification accuracy of multiple classifications lower than a certain threshold into the misjudgment set. When the method of the embodiment of the disclosure is applied, if the object to be classified is similar to a certain object in the misjudgment-prone set, the object is classified without using a classification model.
According to an embodiment of the present disclosure, the object to be classified may have a plurality of attributes. The following describes, with reference to fig. 3A and fig. 3B, whether the object to be classified is determined to be similar to the object in the misjudgment prone set based on the misjudgment prone set according to the embodiment of the present disclosure.
Fig. 3A and 3B schematically show a flowchart for determining whether the object to be classified is similar to an object in the misjudgment-prone set based on the misjudgment-prone set according to an embodiment of the present disclosure.
As shown in fig. 3A, the method includes operations S310 to S330.
In operation S310, it is determined whether each attribute of the object to be classified is similar to each attribute of the objects in the misjudgment susceptible set, respectively.
In operation S320, the number of attributes of the object to be classified that are similar to the objects in the set of easy misjudgment is determined.
In operation S330, it is determined whether the object to be classified is similar to the objects in the misjudgment prone set by comparing the ratio with a preset threshold.
Please refer to fig. 3B. As shown in fig. 3B, the method includes operations S301 to S306.
In operation S301, an object is obtained.
In operation S302, attributes of the object are traversed.
In operation S303, it is determined whether the attributes are similar, and if so, operation S304 is performed, otherwise, operation S302 is returned. According to an embodiment of the present disclosure, the similarity of attributes may be defined:
C1＝|Ai，j-A0，j|/A0，j，
wherein A isi，jIs the value of the jth attribute of object i, A0，jA value representing the jth attribute of an object in the set of false positives. The smaller the similarity C, the more similar the jth attribute representing the two objects. A threshold E (0. ltoreq. E. ltoreq.1) may be set, and when C. ltoreq. E, A is considered to bei，jAnd A0，jSimilarly.
In operation S304, the count parameter is incremented by 1 for counting the number of similar attributes between the two objects.
In operation S305, it is determined whether traversal is completed, and if so, operation S306 is performed, otherwise, operation S302 is returned to.
In operation S306, a similarity between two objects is calculated.
According to an embodiment of the present disclosure, the similarity of objects may be defined:
C2＝S/N，
wherein, N is the number of the object attributes, and S is the number of the similar attributes, and can be obtained by reading the counting parameters. A threshold B (0. ltoreq. B. ltoreq.1) can be set if C2And B, considering that the object is similar to the object in the misjudgment-prone set, and if B is larger, representing that the two samples are more similar.
Under the condition of numerous attributes, a space with hundreds of dimensions is needed for calculating the similarity by using a general Euclidean distance, and the judgment similarity threshold is not well determined.
Reference is made back to fig. 2. In operation S230, in case that the object to be classified belongs to an object that is misjudged, the object to be classified is classified based on a second predetermined rule.
According to the embodiment of the present disclosure, the method further includes classifying the object to be classified through the trained classification model in a case that the object to be classified does not belong to an object that is prone to misjudgment.
According to an embodiment of the present disclosure, in the case that the object to be classified belongs to an object that is prone to be misjudged, classifying the object to be classified based on a second predetermined rule includes, in the case that the object to be classified is similar to an object in the set that is prone to be misjudged, classifying the object to be classified based on a classification result of the object in the set that is prone to be misjudged. For example, in the case that the correct classification result of the objects in the misjudgment prone set is known, if the object a to be classified is similar to the object B in the misjudgment prone set, and the object B belongs to the X category, according to the embodiment of the present disclosure, the object a to be classified may not be sent to the classification model, but the object a to be classified may be directly determined as the X type consistent with the classification result of the object B.
According to the method of each embodiment disclosed by the disclosure, whether the object to be classified belongs to the object which is easy to misjudge is judged in advance, if the object belongs to the object which is easy to misjudge, the classification result is determined directly based on the second preset rule without passing through the classification model, so that the situation of wrong classification is reduced to a great extent, and the classification accuracy is improved.
Fig. 4 schematically illustrates a block diagram of a classification system 400 according to an embodiment of the present disclosure.
As shown in fig. 4, the classification system 400 includes an obtaining module 410, a first classification module 420, and a second classification module 430.
The obtaining module 410, for example, performs operation S210 described above with reference to fig. 2, for obtaining the object to be classified.
The first classification module 420, for example, performs operation S220 described above with reference to fig. 2, for determining whether the object to be classified belongs to a misjudged object based on a first predetermined rule.
The second classification module 430, for example, performs the operation S230 described above with reference to fig. 2, for classifying the object to be classified based on a second predetermined rule if the object to be classified belongs to a misjudged object.
According to the embodiment of the present disclosure, the system further includes a third classification module, configured to classify the object to be classified through the trained classification model when the object to be classified does not belong to an object that is prone to misjudgment.
According to the embodiment of the disclosure, the first classification module comprises a similarity judgment sub-module and a first classification sub-module. And the similarity judgment submodule is used for determining whether the object to be classified is similar to the object in the misjudgment-prone set or not based on the misjudgment-prone set. And the first classification submodule is used for determining that the object to be classified belongs to the object which is easy to misjudge under the condition that the object to be classified is similar to the object in the set which is easy to misjudge.
According to the embodiment of the present disclosure, the second classification module includes a second classification sub-module, configured to classify the object to be classified based on the classification result of the objects in the misjudgment prone set when the object to be classified is similar to the objects in the misjudgment prone set.
Fig. 5 schematically illustrates a block diagram of a similarity determination sub-module 500 according to an embodiment of the present disclosure.
As shown in fig. 5, the similarity determination sub-module 500 includes an attribute comparison unit 510, a duty determination unit 520, and a similarity determination unit 530.
According to an embodiment of the present disclosure, the object to be classified has a plurality of attributes.
The attribute comparison unit, for example, performs operation S310 described above with reference to fig. 3A, for determining whether each attribute of the object to be classified is similar to each attribute of the objects in the misjudgment susceptible set, respectively.
The proportion determining unit, for example, performs operation S320 described above with reference to fig. 3A, for determining the number proportion of the attributes of the object to be classified that are similar to the objects in the set of easy misjudgments.
The similarity determination unit, for example, performs operation S330 described above with reference to fig. 3A, to determine whether the object to be classified is similar to the object in the misjudgment set by comparing the proportion with a preset threshold.
According to the embodiment of the disclosure, the object to be classified is network flow data, and the obtaining module comprises a obtaining submodule and a processing submodule. And the obtaining submodule is used for obtaining the data of the transmission layer of the network. And the processing submodule is used for processing the data of the transmission layer to obtain network flow data.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any plurality of the obtaining module 410, the first classifying module 420, the second classifying module 430, the third classifying module, the similarity judging sub-module, the first classifying sub-module, the second classifying sub-module, the attribute comparing unit 510, the proportion determining unit 520, the similarity judging unit 530, the obtaining sub-module, and the processing sub-module may be combined and implemented in one module, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to the embodiment of the present disclosure, at least one of the obtaining module 410, the first classifying module 420, the second classifying module 430, the third classifying module, the similarity judging sub-module, the first classifying sub-module, the second classifying sub-module, the attribute comparing unit 510, the proportion determining unit 520, the similarity judging unit 530, the obtaining sub-module, and the processing sub-module may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware such as any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three manners of software, hardware, and firmware, or by a suitable combination of any of them. Alternatively, at least one of the obtaining module 410, the first classifying module 420, the second classifying module 430, the third classifying module, the similarity judging sub-module, the first classifying sub-module, the second classifying sub-module, the attribute comparing unit 510, the proportion determining unit 520, the similarity judging unit 530, the obtaining sub-module, and the processing sub-module may be at least partially implemented as a computer program module, which may perform a corresponding function when executed.
FIG. 6 schematically illustrates a block diagram of a computer system 600 suitable for implementing the classification method and system according to an embodiment of the present disclosure. The computer system illustrated in FIG. 6 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure. The computer system shown in fig. 6 may be implemented as an electronic device including at least one processor (e.g., processor 601) and at least one memory (e.g., storage 608).
As shown in fig. 6, a computer system 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. Processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 601 may also include onboard memory for caching purposes. Processor 601 may include a single processing unit or multiple processing units for performing different actions of a method flow according to embodiments of the disclosure.
In the RAM 603, various programs and data necessary for the operation of the system 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or RAM 603. It is to be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, system 600 may also include an input/output (I/O) interface 605, input/output (I/O) interface 605 also connected to bus 604. The system 600 may also include one or more of the following components connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program, when executed by the processor 601, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable medium, which may be embodied in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer readable medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, a computer readable medium may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, optical fiber cable, radio frequency signals, etc., or any suitable combination of the foregoing.
For example, according to an embodiment of the present disclosure, a computer-readable medium may include the ROM 602 and/or the RAM 603 and/or one or more memories other than the ROM 602 and the RAM 603 described above.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.
Claims (14)
1. A method of classification, comprising:
obtaining an object to be classified;
determining whether the object to be classified belongs to an object which is easy to misjudge or not based on a first preset rule;
and under the condition that the object to be classified belongs to the object which is easy to misjudge, classifying the object to be classified based on a second preset rule.
2. The method of claim 1, further comprising:
and under the condition that the object to be classified does not belong to the object which is easy to misjudge, classifying the object to be classified through the trained classification model.
3. The method of claim 1, wherein the determining whether the object to be classified belongs to a misjudged object based on a first predetermined rule comprises:
determining whether the object to be classified is similar to the object in the misjudgment prone set or not based on the misjudgment prone set;
and under the condition that the object to be classified is similar to the objects in the misjudgment prone set, determining that the object to be classified belongs to the misjudgment prone object.
4. The method according to claim 3, wherein the classifying the object to be classified based on a second predetermined rule in case the object to be classified belongs to a misjudged object comprises:
and under the condition that the object to be classified is similar to the object in the misjudgment prone set, classifying the object to be classified based on the classification result of the object in the misjudgment prone set.
5. The method of claim 3, wherein the object to be classified has a plurality of attributes, and wherein determining whether the object to be classified is similar to objects in the misjudge set based on the misjudge set comprises:
respectively determining whether each attribute of the object to be classified is similar to each attribute of the object in the misjudgment-prone set;
determining the number proportion of the attributes of the object to be classified, which are similar to the objects in the misjudgment-prone set;
and comparing the ratio with a preset threshold value to determine whether the object to be classified is similar to the object in the misjudgment-prone set.
6. The method of claim 1, wherein the object to be classified is network flow data, and the obtaining the object to be classified comprises:
obtaining data of a transmission layer of a network; and
and processing the data of the transmission layer to obtain network flow data.
7. A classification system comprising:
an obtaining module for obtaining an object to be classified;
the first classification module is used for determining whether the object to be classified belongs to an object which is easy to misjudge or not based on a first preset rule;
and the second classification module is used for classifying the object to be classified based on a second preset rule under the condition that the object to be classified belongs to an object which is easy to misjudge.
8. The system of claim 7, further comprising:
and the third classification module is used for classifying the object to be classified through the trained classification model under the condition that the object to be classified does not belong to the object which is easy to misjudge.
9. The system of claim 7, wherein the first classification module comprises:
the similarity judgment submodule is used for determining whether the object to be classified is similar to the object in the misjudgment prone set or not based on the misjudgment prone set;
and the first classification submodule is used for determining that the object to be classified belongs to the object which is easy to misjudge under the condition that the object to be classified is similar to the object in the set which is easy to misjudge.
10. The system of claim 9, wherein the second classification module comprises:
and the second classification submodule is used for classifying the object to be classified based on the classification result of the objects in the misjudgment-prone set under the condition that the object to be classified is similar to the objects in the misjudgment-prone set.
11. The system of claim 9, wherein the object to be classified has a plurality of attributes, the similarity determination submodule comprising:
an attribute comparison unit, configured to determine whether each attribute of the object to be classified is similar to each attribute of the objects in the misjudgment-prone set;
the proportion determining unit is used for determining the proportion of the number of the attributes of the objects to be classified, which are similar to the objects in the misjudgment set;
and the similarity judgment unit is used for comparing the ratio with a preset threshold value to determine whether the object to be classified is similar to the object in the misjudgment-prone set.
12. The system of claim 7, wherein the object to be classified is network flow data, the obtaining module comprising:
the obtaining submodule is used for obtaining the data of a transmission layer of the network; and
and the processing submodule is used for processing the data of the transmission layer to obtain network flow data.
13. An electronic device, comprising:
one or more processors;
a memory for storing one or more computer programs,
wherein the one or more computer programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1 to 6.
14. A computer readable medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 6.
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