The Research of Tax Text Categorization based on Rough Set

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1 Avalable onlne at Physcs Proceda 33 (01 ) Internatonal Conference on Medcal Physcs and Bomedcal Engneerng The esearch of Tax Text Categorzaton based on ough Set Bn Lu, Guang Xu, Qan Xu and Nan Zhang Electrcal and Informaton Engneerng College Shaanx Unversty of Scence and Technology X an 71001, P.. Chna blue-prnt@16.com; formated@16.com Abstract To solve the problem of effectve of categorzaton of text data n taxaton system, the paper analyses the text data and the sze calculaton of key ssues frst, then desgns text categorzaton based on rough set model Publshed by Elsever by Elsever B.V. Selecton Ltd. Selecton and/or peer and/or revew peer-revew under responsblty under responsblty of ICMPBE Internatonal of [name Commttee. organzer] Open access under CC BY-NC-ND lcense. Keywords:ough Set, Tax Categorzaton, Text, Data Mnng Introducton The rough set theory (ough Sets Theory ST) [1] proposed by PawlakZ n 198 s an effectve means of knowledge dscovery. Compared wth other tools, ST enjoys many rreplaceable superorty and "let the data tself speak" s the outstandng advantages of rough set theory. Because of ths, rough set theory has become a powerful tool for data analyss and knowledge acquston n recent years, whch has been the attenton of many scholars at home and abroad. Internatonally, the European countres place great emphass on theoretcal research, and North Amercan s applcaton-orented, whle Japanese ntegrate rough sets and probablty theory and are promnent n medcne applcaton; Chnese scholars has been n a leadng poston n the calculaton of gran n the world []. Overall, based on the classcal theory of rough sets has been perfected, and has bascally formed a relatvely complete theoretcal system [3-6]. Ths work s partally supported by educaton department of shaanx provncal government specal research project Publshed by Elsever B.V. Selecton and/or peer revew under responsblty of ICMPBE Internatonal Commttee. Open access under CC BY-NC-ND lcense. do: /j.phpro

2 1684 Bn Lu et al. / Physcs Proceda 33 ( 01 ) However, n realty, t s dffcult to acheve accurate and complete database. To overcome the mpact of naccurate, ncomplete data on the knowledge acquston, researchers have proposed and studed varable precson rough set (VPS) model of Bayesan rough set (BS) model and ncomplete nformaton systems knowledge acquston, better solutons to the data by the nose polluton and the ncomplete [7]. Although these studes have acheved many mportant results, there are stll some mportant ssues to be resolved, especally neffcency about reducton algorthm, whch has lmted further applcatons expanson of the rough set theory. So, faced wth dynamc, vast amounts of tax data to fnd effectve methods of knowledge acquston has more pressng needs, on the other hand sgns of drft data of the rough set theory and method of taxaton has not been found. If the labor s to complete the categorzaton of the orgnal text of tax data, t may be more accurate, yet tme and labor consumng, and we can not rule out the subjectve nterference. Usng computer to handle a large number of text categorzaton may reduce the burden and mprove the utlzaton of nformaton. Text Categorzaton Based on ough Set Text categorzaton s an mportant research area n Chnese nformaton processng. After text analyss, they are assgned to the more approprate categores, so as to enhance the text retreval, text processng effcency of storage and other applcatons. The sources of these texts are dverse, lke reports, documents, news and emal, etc. The number of text type can be ether pre-determned, or determned after gong through the organzaton of the document. Many text categorzaton methods are appled to the feld, such as support vector machne (SVM), K nearest neghbor method (KNN), Nave Bayes (Nave Bayes), decson tree (Decson Tree) and so on. [8-1] However, n text categorzaton processng, each text s for a partcularly hgh Dmenson vector, although t expressed all the ssues, but even the most powerful processng computers also fnd t dffcult to process and compare the calculated vectors wth further ncrease of classfed documents. In order to reduce the dmenson of document vectors, many of the statstcs adopt flterng methods n the frequency threshold, whch can reduce the vector dmenson, but nevtably lose some useful nformaton, especally for the categorzaton of mportant low-frequency words (such as certan types of proper names, although the frequency s low, but the dstncton between the roles of type large) and ultmately affect the categorzaton accuracy. Categorzaton model based on rough set theory text uses rough sets for knowledge equvalent to dvde thought, keepng the nformaton of concept text. After dvdng many dfferent categores from the text, the most basc keyword vectors are extracted from the text categores as a prerequste for the rule, the type of document as decson-makng rules. Makng use of the knowledge educton theory n the proposed text categorzaton rules to valdate the new text, then the fnal output wll meet the categorzaton requrements of the rules. Ths categorzaton rule s easy to understand, so that knowledge systems smplfy the process.[13] Text messages, and granular computng A. The defnton of sze and boundary nformaton Informaton granularty s detaled nformaton and knowledge at dfferent levels of measurement, many felds, the concept of nformaton granularty, an mportant feature of artfcal ntellgence s the ablty to observe from dfferent sze and analyze the same queston, the world dealng wth dfferent partcle sze problem-solvng. [14] ough set beleves that the sze of acqured knowledge, can not accurately represent some of the concepts and can not dstngush between the resultng object. Ths leads to the so-called on the mprecse

3 Bn Lu et al. / Physcs Proceda 33 ( 01 ) "border" deology. Famous phlosopher Frege that "the concept must have a clear boundary, there s no clear boundary wll correspond to a boundary lne around the area not explctly." ough s the focus of the ambguty of the boundary based on the concept of an mprecse concept that has not been clearly defned fuzzy boundares. To characterze ths ambguty, ough set approxmaton usng two precson set to come to that. Set X U AT where the lower approxmaton nd X and upper approxmaton nd X of X s: X x U I ( x) X x X I ( x X nd ) nd X x U I x) X I ( x ) ( x X Lower approxmaton nd X s the use of knowledge, all of U s the elements of a varable can be assgned to the set X ; upper approxmaton nd X s the use of knowledge, U may be assgned to X all the elements. The set pos ( X ) nd ( X ) s called the doman of X the negatve doman of X s neg( X ) U nd ( X ) bn ( X ) nd X nd X s the doman boundares of X Postve regon pos (X ) s the knowledge of, Is completely determned by a collecton of classfed X. The negatve doman neg (X ) s that the knowledge doman mpossble collecton of objects belongng to X, whch belongs to the complement of X. On the doman boundary regon s a sense of uncertanty doman, the doman knowledge for objects belongng to the border demarcaton can not be determned are X or X. The upper approxmaton s the knowledge that the X can not rule out the possblty that they belong to the object composton, from the formal pont of vew, the approxmaton s the postve regon and boundary regon and sets. As can be seen from the above defnton, ambguty and uncertanty n connecton wth ths, that ambguty s expressed n the uncertanty. In general, n a gven approxmaton space, not all objects are avalable for a gven subset of the knowledge to be expressed as the concept of a subset of ths s rough on that concept. However, the concept of rough concepts can be roughly two precse defntons, whch enables us to accurately descrbe mprecse concepts.+ B. Calculaton of partcle sze Tradtonal granular computng man steps are as follows:[15] Set U s the Non-empty fnte set of domans, s the attrbute set, denoted U / X 1, X,, X r, Sad categorzaton ablty, known as the knowledge of, use of resoluton matrx M () can determne whether the object resoluton n order to estmate the resolvng power of knowledge. Defnton 1: set DIS () s dstngush degrees of knowledge,and DIS( ) mj ( ) / M ( ). Where m j () s Identfy the number of matrx elements of m j (), () number of elements contaned n resoluton matrx, where 0 DIS ( ) 1 1/ U. esoluton reflect the sze of the resolvng power of knowledge. M s the M ( ) U, then

4 1686 Bn Lu et al. / Physcs Proceda 33 ( 01 ) Defnton : set GD () s the granularty of the knowledge, then r GD( ) X / U 1 Where X s the the base equvalence class os X, then granularty of knowledge and knowledge of and dstngush the followng relatonshp between the degree of GD ( ) 1 DIS( ). X ( r) equvalence class of elements n the attrbute set s ndstngushable, Then to the followng conclusons: X s the equvalence class and column at the ntersecton of X Text Categorzaton Based on ough Set Model 1,,, r X s the element M (), M () s the row X, Otherwse. Concentraton does not exst because of the rough to the document vector synthetc vectors for the categorzaton of problem because of the decson table as a matrx of the whole operaton s carred out, can effectvely prevent loss of nformaton synthess process. Meanwhle, the rough set on the hghfrequency words and low-frequency words can be a good deal. In the decson table, f a characterstc frequency n the dfferent categores of tems n the more volatle, ndcatng that ths feature tems made great contrbutons to the categorzaton, the other hand, f a feature tem n the dfferent categores of dstrbuton, regardless of ther frequency hgh or low, s no contrbuton for categorzaton, these features can be about smple swap tems. In ths process, the frequency of sze wll no longer be mportant, the decsve factor s the frequency of the promulgaton. Therefore, the use of text categorzaton based on rough set model reducton, the categorzaton s not very mportant to flter out low frequency words, do not keep no dstncton between hgh-frequency words. The proposed text categorzaton based on rough set model, conssts manly of tranng, two-part test, fnally used the traned classfer to classfy the new text. Tranng s the core of the text categorzaton system, the purpose of testng by experts and some text text categorzaton system for comparng the categorzaton made to measure the effect of the text categorzaton system. Usng the results of testng the correctness of the document verfcaton tranng, f the test result s greater than a set categorzaton accuracy (threshold), the output rules, the end of operaton, or need to return to the feature vector extracton re-calculate the weght, select a new feature sub set, repeat the process untl the results are satsfed. Enter the tranng process categorzaton system has been dvded nto many dfferent categores from the text nformaton set, the output s a categorzaton system of the process shown n Fgure 1. Concluson ough set method has some advantages compared tradtonal methods, data and text mnng areas are playng a growng role n rough set theory s appled to the excavaton there are many areas to be studed. Ths artcle wll focus part of a large number of tax data n the text of the categorzaton of research, analyss of the text nformaton and granular computng the key ssues, set up text categorzaton based on rough set model, and gves the process of text categorzaton system. Compared to the exstng text categorzaton methods, both n the feature tem s the selecton of data, or n the categorzaton accuracy of all have a better performance. In order to acheve the tax categorzaton text desgn deas proposed for data mnng applcaton n the tax provdes a good reference.

5 Bn Lu et al. / Physcs Proceda 33 ( 01 ) Input: word Informaton Set Word pretreatment Preprocessng rule Database Feature vector extracton and weght calculaton Evaluaton functon Constructon nformaton decson table Decson attrbutes and values of the reducton No Verfy the accuracy of the Decson rules Yes Categorzaton Algorthm Output: Category esults Fg. 1 flow chart of the system. eferences [1] Pawlak Z. ough Sets. Internatonal Journal of computer and nformaton Scences, 198, pp [] Pawlak. Z, Slownsk.. ough set approach to multattrbute deeson analyss (nvtedrevew) [J]. EuroPean Journal of Operatonal eseareh. 1994, pp [3] Chang l-yun, Wang guo-yn, Wu Yu. An Approaeh for Attrbute educton and ule Generaton Based on ough Set Theory[J]. Journal of Software. 1999, pp [4] Shepard N.,Novland C.,Jenkns H.. Learnng and memorzaton of categorzaton. Psychologcal Monographs,1961, pp.1-4. [5] Nosofsky M.,Palmer J.,Mcknley C.. ule-plus-excepton model of categorzaton learnng. Psychologcal evew,1994, pp

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