A Study On The Data Mining Model For The Tax Assessment

With the continuous development of the socialist market economy and thegradual deepening of Chinese tax management, tax authorities of all levelshave accumulated considerable historic tax-related data in a period. How toget meaningful information from these data has become an important task oftaxation under the conditions of knowledge. In this context, academia and theauthorities have started useful researches of tax assessment from aspects ofeconomics, management, information technology, etc.The dissertation defines why, what and how to do with problems in theresearch on the tax assessment with the framework of tax compliance theoryand micro revenue analysis theory from the point of view of tax authorities,which lays the theoretical foundation for the follow-up research. Next, thebasic theory of the data mining is expatiated and a process model of data miningtechniques application in the tax assessment is explained. Then a user specificbased process model of data mining in application is put forward after theanalysis of the shortcomings of the existing two process models of data mining.The model provides a reference standard for the application of model of datamining. The dissertation brings forward building the model of data miningdynamically based on reduction of rough set attributes and makes ademonstrative research for the model with the example of VAT and defines thetax assessment index system as a knowledge system. Next, the dissertationconstructs a identification model for tax assessment rough setneural network, taking the index after reduction as the variables input. Theresult of contrast of demonstration research points out the problem ofgeneralization ability and two kinds of problems of identification incorrectlywhile showing the effectiveness of the rough set to the index systemoptimization. In light of these problems, the dissertation puts forward theidentification model of neural network ensemble. The result of demonstrationresearch shows that the model can improve the generalization ability of theidentification model effectively and reduce the rate of two types of error.The article putting forward the problem. This chapter sets forth the basic concepts of tax assessment, the course of development before summarizingcurrent problems of the tax assessment management issues, points out thepurpose and significance of the research. Then the dissertation makescomprehensive analysis and comment on current domestic and abroad taxassessment status search, exemplifies the deficiency and the content of theresearch. Finally, it discusses the main content and the main framework.This explains basic concepts of data mining, the difference between datamining and knowledge discovery in database and models and approaches of datamining. Then, it analyzed two main process models: technology-oriented Fayyadand application-oriented CRISP-DM,points out problems when applying thesetwo models in tax assessment: the lack of specialization of all data miningtasks results in the deficiency of the process support. Subsequently, thearticle defines the role of users according to the practice of data miningin tax assessment process and tasks of data mining process, putting forwarda user-specific-based process model of data mining, expatiating the content,providing a reference standard for the application of model of data mining.Research on the model construction of the index system in tax assessment.The first summarizes the research of index system in tax assessment and itsconstruction, points out three problems of strong subjective empirical, lackof effective methods and a relatively static index system and puts forwardsa new idea to construct the index system by integrating qualitative analysisbases on micro revenue analysis theory with quantitative methods base on datamining. Next to VAT for example, the method based on reduction of rough setattributes to optimize the tax assessment indicator set. From the point ofview of dynamic change of assessed objects, gives the definition of taxassessment index system in the knowledge system, puts forward dynamic indexsystem construction model based on reduction of rough set attributes andexplains its principle and process of dynamic regulation.Research on the identification model of tax assessment, constructsrough set-neural network identification model after summarizing andanalyzing the analyzing method of peak value, discriminant analysis, expertsystem and neural network to identify tax assessment object, trains andsimulates it with the actual data from VAT tax assessment. The result ofverifying and analysis shows that the identification effect of the model isnot very satisfactory, lacking a solution to problems of identifying two errors. Next, a 8-2 absolutely vote method to generate conclusion of ensemble learningis put forward based on the method of neural network ensemble learning. Aidentification model of neural network ensemble learning is designed withdemonstration research, with the result that the model enhancesthe generalization ability effectively and reduce the rate ofidentification incorrectly. It is feasible to apply in the identification oftax assessment objects.(1) To define why, what and how to do with problems in the research ofthe tax assessment with the framework of tax compliance theory and microrevenue analysis theory from the point of view of tax authorities. (2) Researchthe process model of data mining in tax assessment, and put forward auser-specific-based process model of data mining in tax assessment. (3) Withregard to tax assessment index system, put forward a dynamic construction modelbased on reduction of rough set attributes, give the definition of the taxassessment index system in the knowledge system. (4) To put forward aidentification model based on neural network ensemble to enhancesthe generalization ability of neural network models and reduce the rate ofidentification incorrectly. Finally, the chapter prospect the issue which needfurther research in the future.