Fuzzy Modeling for Multi-Label Text Classification Supported by Classification Algorithms

Transcription

1 Journal of Computer Senes Orgnal Researh Paper Fuzzy Modelng for Mult-Label Text Classfaton Supported by Classfaton Algorthms 1 Beatrz Wlges, 2 Gustavo Mateus, 2 Slva Nassar, 2 Renato Cslagh and 3 Rogéro Cd Bastos 1 Department of Informaton Systems, Federal Unversty of Santa Catarna, Floranópols, Brazl 2 Department of Informats and Statst, Federal Unversty of Santa Catarna, Floranópols, Brazl 3 Department of Engneerng and Knowledge Management, Federal Unversty of Santa Catarna, Floranópols, Brazl Artle hstory Reeved: Revsed: Aepted: Correspondng Author: Beatrz Wlges Department of Informaton Systems, Federal Unversty of Santa Catarna, Floranópols, Brazl Emal: beatrz.wlges@ufs.br Abstrat: The ever-nreasng amount of nformaton on the Web s organzed n strutured, sem-strutured and unstrutured data. Text lassfaton systems, apable of handlng suh dfferent strutures, may faltate the work of mportant tasks suh as ndexaton and nformaton retreval n searh engnes. The objetve of ths researh s to develop a method for the lassfaton of douments nto multple ategores wth fuzzy log. Ths method was bult from a proess of pattern reognton and, also, two varables alled smlarty and auray were used. The proposed fuzzy lassfaton method uses varables that express the ablty to analyze the smlarty and auray of a doument through a database of terms. The database of terms s generated by a olleton of pre-lassfed douments n ategores of nterest. The douments proessed aordng to the smlarty and auray n the database of terms omposes a tranng set also alled knowledge base. From ths database, t s possble to dentfy a pattern that spefes a set of rules through a knowledge dsovery proess. Ths proess nvolves the data mnng of the knowledge base. Thus, t was possble to defne a general model that s used n the reaton of rules and membershp funtons of the fuzzy model for the lassfaton of douments nto multple ategores. The general model of the rules dentfed n the data mnng proess and mplemented n fuzzy model onsders the most sgnfant varables and also ontrbutes to the spefaton of the membershp funtons, suh as the defnton of lngust terms of fuzzy sets. Thus, t was possble to mplement a more determnst approah regardng the nput, membershp funtons and nferene rules of the fuzzy model. The results of the proposed method for lassfaton of douments are relevant beause they have a satsfatory auray rate. Keywords: Text Categorzaton, Deson Tree, Fuzzy Modelng Introduton The volume of nformaton avalable s nreasng sgnfantly over the years, whh means that people have more aess to knowledge from any eletron deve over the Internet. Thus, automat ategorzaton systems play an mportant role n the text lassfaton proess, by assstng n nformaton retreval proesses. Aordng to Yang and Pedersen (1997) the proess of retrevng douments n properly lassfed databases s more effent and the searh sope s redued even f a large volume of nformaton s avalable. L et al. (2011) further state that the goal of text lassfaton s to label textual douments wth themat lasses from a predefned set. Also aordng to these authors, many dfferent methods have been appled to text lassfaton tasks nludng the K-Nearest Neghbor (KNN) approah (Bang et al., 2006; Tan, 2006), Naïve Bayesan approahes (Baker and MCallum, 1998; Lews, 1998; Yang and Lu, 1999), support vetor mahne (Dumas and Chen, 2000) and deson trees (Lews and Rnguette, 1994; Qunlan, 1993). However, most mahne learnng algorthms were desgned for sngle-label lassfaton n whh a doument an only belong to one ategory (Jang et al., 2012). In mult-label text lassfaton, a doument an belong to more than one ategory. Therefore, the fous of 2016 Beatrz Wlges, Gustavo Mateus, Slva Nassar, Renato Cslagh and Rogéro Cd Bastos. Ths open aess artle s dstrbuted under a Creatve Commons Attrbuton (CC-BY) 3.0 lense.

2 Beatrz Wlges et al. / Journal of Computer Senes 2016, 12 (7): DOI: /jssp ths researh s on mult-label text lassfaton assoated wth fuzzy models. The goal of ths researh s to analyze a mult-label text lassfaton proess, whh generated a fuzzy model. In the researh (Peters and Koster, 2003) was used a new rteron for Term Seleton, whh s based on the unertanty n Term Frequeny aross ategores. Ths proposal nvolved onstrutng a database of terms organzed by ategory, where lassfaton algorthms were appled to generate Deson Trees (DT) from several text lassfaton tests. Wth these DT, t was possble to generate a lned up fuzzy model apable of dsplayng the relevane degrees of the analyzed text for eah ategory. Thus, a database was bult for eah ategory usng unstrutured text extrated from the Web, mostly onlne magaznes and newspapers. Ths database was organzed nto four ategores nvolvng Eduaton, Tehnology, Sports and Eonomy. In ths researh, the most frequent terms n eah ategory were onsdered. Ths artle s organzed as follows: Intally, the onstruton of the base of terms from dfferent soures wll be presented, wth an estmate of approxmately 3000 words have been used for eah ategory. In sequene, the methodology of the lassfaton proess was detaled as well as ts varables and ndators. Followng, the lassfaton proess and ther results usng Deson Tree algorthms (DT) are presented. The fuzzy model bult from the lassfaton proess s presented next and fnally, onsderatons and further analyss on fuzzy modelng are suggested. General Organzaton Model The proposed model wll allow a olleton of douments from the OM to be lassfed aordng to ategores of nterest to the organzaton. For ths, a method of doument lassfaton that onsders multple ategores s appled. To perform the mplementaton of ths organzatonal model, we developed a method of doument lassfaton. Ths method onsders the hgh dmensonalty nvolved n the lassfaton of douments,.e., as a doument ontans lots of nformaton that an be represented n several lasses, t s ategorzed n multple ategores. Fgure 1 shows the steps of the development of the doument organzaton model based on ths method. Also at ths stage the varables are defned and the data to a knowledge base s generated wth these varables. Ths database s bult to allow the knowledge dsovery proess fnd assoatons between varables n order to desgn rules for the fuzzy modelng. So part of these atvtes nvolve a proess of Knowledge Dsovery n Databases (KDD), spefally n a learnng set dvded nto tranng and testng. The proessng on ths tranng set s done by rankng algorthms. The evaluaton of the model generated n ths phase s done by alulatng the auray and error. Both auray and error of the model onsder the results of the mathes between the real lass and the expeted lass. The proposed method onsders a proess of Knowledge Dsovery (KDD) on the rules that are mplemented n a fuzzy modelng. The use of fuzzy log deals wth overlappng ategores n a doument handlng the vagueness of the nput varables. The struture of the doument organzaton model s shown n Fg. 2. The applaton of the model s made from a preproessed doument, whose smlarty and onfdene are alulated aordng to the defnton of a database of terms. These two varables are the nputs of the fuzzy modelng. Thus, a olleton of douments of an organzaton an be ategorzed by assgnng ts relevane to eah ategory dentfed as of nterest to the organzaton. Fg. 1. Model development 342

3 Beatrz Wlges et al. / Journal of Computer Senes 2016, 12 (7): DOI: /jssp Fg. 2. Model applaton Buldng the Term Database After seletng a olleton of douments belongng to the same ategory, the stage of text preparaton ommened. Ths step nvolves ertan tehnques that faltate the proess of seletng textual features. The proposal n order to buld the database (DB) was to dentfy all words that best express the haratersts of a gven ategory. Ths DB s onstruted based on the most ommon terms n all douments of the same ategory. In ths ase, the doument database should be large enough to over most terms that defne the ategory. From that DB t s possble to defne f any text belongs to a ategory n the DB. Pre-proessng s a very mportant stage n the text lassfaton, nludng several steps to transform the set of douments, n natural language, nto a lst of useful terms and n a format whh s ompatble for knowledge extraton. In ths study, a tool alled RapdMner (2016) was used to perform both preproessng and DT generaton. Doument proessng frstly nvolved the Extrat Content operator, whh extrats the textual ontent of an HTML doument and yelds extrated text bloks from the doument. Later, the tokenze operator dvdes the text of a doument n a sequene of tokens. Further, the Transform Cases operator transforms all haraters of a doument n lowerase or upperase. Fnally, the Flter Stop words (Dtonary) operator removes all the words that belong to a lst of stop-words, whh s loaded from a fle on the operator tself. The stop-words lst ontans words that are not relevant to ountng the most ommon terms n the text doument; therefore, the lst ontans grammatal words that do not affet the defnton of the ategory of the text. Fgure 3 shows a part of the DB onstruted from doument proessng. In Fg. 3 t s observed that, for every text doument, all terms n respet to ther total ourrene n the text (total ourenes) and whh doument the term was extrated from (doument ourenes) were analyzed. At ths stage of the text doument proessng, studes and heks have been onduted to dentfy the best way to mplement ths proess. It was onsdered a aseomparson study utlzng both seral and parallel paradgms n the text proessng (Wlges et al., 2014). In the results, t was observed the hgher effeny of seralzed proessng. Ths s beause the text fles used n proessng dd not have a sze on the order of petabytes. In ths researh t s lear that beng able to defne whether a text belongs to a ategory s not enough. It s mportant to know the auray of the analyss durng the deson makng proess. Hene, a fuzzy model that ould onsder the membershp degree of a text doument to a partular ategory was developed. Therefore, all terms of eah ategory from the DB were ordered and normalzed to the hghest and lowest frequeny for eah ategory. Thus, an ndex alled j relevane degree ( w ) j The relevane degree ( w ) RD was obtaned for eah word. RD of eah word (w) must be alulated for all ategores (), whh are omposed by many text douments. Thus, the weght of the terms n (DB) for eah ategory, known as the relevane degree j of the term( RD w ), s alulated as follows: RD j w Where: j RD w f ( w,j) = (1) max f ( w z,j) f ( w,j) max( f ( w )) z,j ( ) = Relevane degree of the term w n the ategory j = Term frequeny w n the ategory j = Hghest frequeny represented by the term w z n the ategory j 343

4 Beatrz Wlges et al. / Journal of Computer Senes 2016, 12 (7): DOI: /jssp relaton to ther relevane degree for eah ategory n DB. For eah evaluated word n the analyzed text the respetve RD has been observed: w V = f * RD (2) w w w where, V w represents a value for eah word alulated as a funton of frequeny RD f the word. From the w of V w fw and the relevane degree V of eah word the average w from the analyzed text has been extrated: AV nw 1 = Vw nw Σ (3) =1 Fg. 3. DB wth terms of a ategory For eah of the analyzed ategores n the doument organzaton model, the database should store keys to dentfy the doument, to dentfy the ategory, the representaton of the term, the frequeny of the term n the ategory and the relevane degree n the ategory. The purpose of the terms database s to dentfy all the words that best express the haratersts of a gven ategory. Ths database s bult aordng to of the most ommon terms between the varous douments of the same ategory. Thus, Defnton of varables f a partular ategory has at least 100 thousand words, the desgned database should have a mnmum sample sze of 380 words per ategory, onsderng a samplng error margn of 5% wth a 95% onfdene level. Defnton of Varables Many studes related to Informaton Retreval (IR) work wth models of doument ndexng, doument representaton and smlarty measures from retreved douments. In ths researh, the onepts of smlarty and auray were used. That s, smlarty S between the partes nvolved: The Parsed Text () and the texts from the (DB). In turn, auray A measured the relablty of results presented by smlarty. The Parsed Text () went through the same text doument preparaton proess presented prevously: Extraton, leanup, stop-words removal and harater ase transformaton. From the frequeny of terms n, multplaton by the relevane degree of the term w n eah ategory from DB w was performed. Equaton 2 expresses the relatonshp between the terms of () and the terms of (DB), where fw s frequeny and RD w s the relevane degree of the term for a spef ategory of the DB. TheV stores the frequeny values of n w C Also, the average has been extrated from all RD of eah word of a gven relevane degrees ( w ) ategory from the database: ARD = DB nw 1 RDw nw Σ (4) =1 The smlarty S of the Parsed Text () regardng to the DB Texts s alulated aordng to the average AV T of, alulated by Equaton 3. values ( P ) The AVs dvded by the Average Degree ( DB) terms for eah ategory of the DB. Thus, through Equaton 5: DB ARD of S s obtaned AV S = (5) ARD Confdene s the number of terms of the analyzed text () orrespondng to the database (DB) for eah ategory. Thus the alulaton s represented by Equaton 6: A ( nw nw ) = 1 (6) nw C DB DB Where: nw DB = Total terms for eah ategory of DB nwdb = Total terms whh are ommon between the DB and the analyzed text () = Total terms from the analyzed text () nw Classfyng Tranng Douments A set of test douments, wthn the ategores spefed n the terms database, s seleted to buld a knowledge base on the results of the alulaton of smlarty varables and auray. Thus, the whole set of ntal test douments 344

5 Beatrz Wlges et al. / Journal of Computer Senes 2016, 12 (7): DOI: /jssp passes through the preproessng step, just as the douments that generated the database of terms. After ths step, the vetor representng the test doument s proessed and values of smlarty and auray for eah ategory under onsderaton are obtaned. Furthermore, the pror ndex of the ategory n eah test doument s onsdered,.e., the ategory from whh the doument has been extrated, s ntally dentfed. Therefore, eah analyzed text has the smlarty( S ) C and auray( A ) taken regardng to all ategores from the DB. Table 1 presents the results of ths proess of extraton from S and A onduted on two texts. The C attrbute belongs used n the table orresponds to the atual text lassfaton based on ther ategory. For example, Text 1 belongs to the ategory Eonomy, thus smlarty and auray were evaluated and assgned to eah of the four ategores stored n the database, aordng to the aforementoned equatons. In all results, the Smlarty equaton ponted exatly whh ategory the text belonged to, wthout error. Auray, as shown by the results, has supported Smlarty when t was not the hghest value, beng at least the seond hghest resultng value. These results were arred for the lassfaton of 352 texts and were onsdered satsfatory to desgn a fuzzy model. To onstrut the fuzzy model all results of smlarty and auray were used from the analyzed texts, aordng to the data presented n Table 1. Data Classfaton Proess From the results shown n Table 1, there was a proess of lassfaton for the 352 texts. The proess of data lassfaton uses Deson Tree (DT) algorthms and ams to provde a more determnst approah to the onstruton of the fuzzy model. The DT s bult onsderng the onept of entropy, whh measures the nformaton level of an attrbute. The smaller the entropy value, the lower the unertanty and the most useful the attrbute s for lassfaton. Fgure 4 shows the tree generated under the tested dataset by applyng the ID3 lassfaton algorthm (Qunlan, 1986) n the RapdMner tool. On ths DT the values of the results from auray and smlarty were normalzed to perentages. Fgure 4 shows the frst analyss of the mnng proess, wth lowest entropy n the smlarty attrbute already ategorzed. The results of varables smlarty and auray were lustered nto three groups: Hgh, medum and low. Due to ths onstruton, the lngust varables nlude hgh, medum and low for both the smlarty and auray attrbutes. That s two thresholds were establshed for eah varable (smlarty and auray). Thus, the lmts that lassfy the results nto hgh, medum and low were obtaned. In Fg. 5 we present the results of the proessng from the seond DT algorthm, C4.5 (Qunlan, 1993), to buld the tree. Fg. 4. Result of the ID3 proess Fg. 5. Result of the C4.5 algorthm Table 1. Results of text analyss Smlarty Auray Belong Text Category (S) (A) (B) 2 Eonomy 1 1 yes 2 Eduaton 0,04 0,09 no 2 Tehnology 0 0 no 2 Sport 0,1 0,09 no 3 Eonomy 1 1 yes 3 Eduaton 0,08 0,8 no 3 Tehnology 0 0 no 3 Sport 0,34 0,85 no The proposal of lassfyng data was performed to adjust, n the best possble way, the membershp funtons and ths was only possble through the DT n Fg. 5. A fuzzy model was generated aordng to ths tree. It s observed that, n ths tree, when the Smlarty s Hgh (SH) and the Auray s Hgh (AH) the text Belongs (BY) to a ategory wth, at least, 85% of ertanty. In ases where the Smlarty s Hgh (SH) and the Auray s Medum (AM) the text belongs (BY) wth about 50% of ertanty to the ategory and onsderng the medum (SM) and low (SL) smlartes, the text does not belong (BN) to the ategory wth ertanty lose to 100%. Thus, the membershp funtons and fuzzy model rules have been adjusted to meet ths purpose. 345

6 Beatrz Wlges et al. / Journal of Computer Senes 2016, 12 (7): DOI: /jssp Table 2. Performane evaluaton matrx of the DT model Real lass Correspondene matrx Yes No Expeted lass Yes 26 6 No 8 88 The model verfaton was onduted wth a set of 32 test douments (Fg. 5). Ths model s represented by the DT that was generated by proessng the tranng set. The results presented by the matrx n Table 2 evaluate the performane of the DT model. The auray and the error of ths model onsder the results of the mathes between the real lass and the expeted lass observed n Table 2 and followed the defntons of the funtons as presented n the methodology of ths study. Thus, the alulaton of the auray from the model resulted n 89%. Ths result was onsdered adequate for the mplementaton of the rules n the fuzzy model based on the DT n Fg. 5. Buldng the Fuzzy Model The proposal of the fuzzy model for text lassfaton n multple ategores s relevant beause t an handle the mpreson of knowledge omng from gaps and blanks present n the dataset. Moreover, fuzzy log s able to summarze the results of a lassfaton onsderng two nputs (smlarty and auray) nto an output wth level of relevane. In ths study, MATLAB (2016) was used to buld and smulate the Fuzzy Model. The MATLAB platform s optmzed for solvng engneerng and sentf problems, moreover t has a Fuzzy Log Toolbox that provdes funtons, apps and a Smulnk blok for analyzng, desgnng and smulatng systems based on fuzzy log. The fuzzy modelng s omposed of a fuzzfaton proess, a knowledge base and a defuzzfaton proess. The knowledge base s represented by the rules, nferene engnes and aggregaton funtons. The struture of the desrbed fuzzy modelng s shown n Fg. 6. The fuzzy model was bult based on the results of the DT that allowed the reaton of a more prese approah for both the nput varables as n the rule base. From that, the fuzzy model was able to treat the unertanty n the lassfaton proess of texts. The nput funton alled smlarty was developed wth the lngust varables hgh, medum and low and the nput funton auray was bult wth only the hgh and medum lngust varables as presented n the lassfaton proess of the DT. The output funton alled belong was developed wth the lngust varables yes, maybe and no. All membershp funtons have been mplemented wth Gaussan dstrbuton funtons, whh have a smple urve. In partular gaussmf funton was used, whh has a bell urve wth maxmum of 1 and mnmum at 0. Fgure 7 shows the overvew of the fuzzy model. Fg. 6. The struture of the fuzzy modelng Fg. 7. Fuzzy modelng n text lassfaton The ontroller of the nferene engne used was Mamdan, mplemented wth the mnmum operator and the defuzzfaton method was the smallest of maxmum. Aordng to Wang (1997), defuzzfaton s the reverse proedure of fuzzfaton. In ths phase, we desgn a mappng from a fuzzy set to a rsp value. There are several defuzzfers, suh as Center of Gravty (Centrod), Center Average, Smallest of Maxma (SoM), Mean of Maxma (MoM) and Largest of Maxma (LoM). In ths study we have used the Smallest of Maxma (SoM) as the defuzzfaton method. Aordng (Yagunuma et al., 2013), when usng fuzzy DL wth onept defntons, the defuzzfaton queres avalable are SoM, MoM, LoM, whh onsder the extremes of maxmum degree. Dependng on the stuaton, they may lose nformaton ompared wth other defuzzfaton methods that are based on the shape of the fuzzy set. In the experments of ths study the results wth the SoM method were more aurate. 346

7 Beatrz Wlges et al. / Journal of Computer Senes 2016, 12 (7): DOI: /jssp The Smallest of Maxma (SoM) represents the hoe of smallest pont of the unverse wth the hghest degree of relevane. We alulate the So Musng the followng equaton: µ = mn( x ), suh thatϕ ( x ) = ϕ SoM A where, ϕ A s the membershp funton of set A. Four nferene rules that over all lngust varables were defned, eah rule onssts of the operator and assoated wth the method mnmum. The aggregaton of the rules s made by the method maxmum. Fgure 8 presents the rule base. The rules shown n Fg. 8 were bult aordng to the defntons presented n the C4.5 deson tree algorthm (Fg. 5). Thus, the membershp funtons of the fuzzy model were adjusted so that the output ould orrespond to the values presented by the DT, aordng to Fg. 9. max, Fg. 8. Fuzzy modelng rules n text lassfaton Fg. 9. Fuzzy model rules adjusted by the deson tree The results n fuzzy model are relevant beause they show the atvaton of the membershp funton n at least two ponts for eah of the nput funtons. Fgure 10 provdes the fuzzy ategorzaton when the smlarty s hgh and auray s hgh, showng smlar results to that of the DT. Also, when the smlarty nput s hgh and auray s medum. Results It s known that there s, usually, a predomnant ategory when performng the lassfaton proess of the doument. Yet, there are other relevant values assoated wth the doument for eah of the ategores onsdered n the doman. These values vary n a range of [0,1]. The results of the model, through the fuzzy lassfer, wth ther respetve nputs for auray and smlarty to eah ategory are shown n Table 3. These results show the synthess of the defuzzfed output from the fuzzy modelng for eah entry. The overall assessment was performed n relaton to the alloaton of the defuzzfed output value for the ategory n the orgnal doument ndexng. In some ases, t s lear that even f the smlarty s relatvely hgh, f the auray does not have a sgnfant value to the ategory, the result of fuzzy output s also not favorable. The fuzzy model prortzes the ombnaton of the values of both of the most sgnfant ndators (smlarty and auray) n the result set. Table 4 shows the summary of the overall performane, onsderng a set of 97 douments n relaton to the pror ndexng of the doument to a partular ategory and the fuzzy output generated by the model. Aordng to Table 4 the ht rate was about 78%, that s, from the 97 douments of the tranng set, 75 had the defuzzfed output value orrespondng to the orgnal ndexng. Table 3. Results of text analyss wth fuzzy output Smlarty Auray Belong Fuzzy Text Category (S) (A) (B) out 2 Eonomy 1 1 yes 0,877 2 Eduaton 0,04 0,09 no 0,121 2 Tehnology 0 0 no 0,265 2 Sport 0,1 0,09 no 0,123 3 Eonomy 1 1 yes 0,877 3 Eduaton 0,08 0,8 no 0,467 3 Tehnology 0 0 no 0,117 3 Sport 0,34 0,85 no 0,490 Fg. 10. Results after applyng the frst and seond rules n the fuzzy model Table 4. General results Indexed ategory Sore Yes 76 No 21 Total

8 Beatrz Wlges et al. / Journal of Computer Senes 2016, 12 (7): DOI: /jssp Conluson In ths researh, a database of terms wth four dfferent ategores of texts was onstruted. Eah term was normalzed wthn ther ategory n order to evaluate ther degree of mportane wthn t. A mult-label text lassfaton proess was bult based on two onepts, smlarty and auray. In the prelmnary results, the model has worked onsstently as expeted whle keepng orrespondene between the text and ts ategory. The most experments n text lassfaton have been made from statstal tehnques. Consderng the advantages of text lassfaton, the deal would be that more experments should be desgned to treat other languages, whle takng nto aount the peulartes of eah, espeally regardng the semant analyss of texts. Aordng to (Mannng et al., 2008) the statstal text lassfaton requre a number of good example douments for eah lass. Furthermore, the need for manual lassfaton sn't elmnated beause the tranng douments ome from a person who has labeled them. In order to nrease the effetveness of the presented results by the statstal analyss of ths researh, a way to mplement a fuzzy model was studed, enablng t to nform the relevane degree of the text to eah ategory. Deson tree algorthms were used to buld a fuzzy model that ould math the settngs of results n a database. Ths approah has served ts purpose, beause the onstruted model represents losely the results aheved wth the analyss. Moreover, t was possble to generate output ndatng the relevane n whh the analyzed text belonged to a gven ategory. Text lassfaton problems are well represented when solutons nvolve fuzzy models, sne lassfaton meets the requred expetatons and also ndate what s the. Aknowledgement We aknowledge the Federal Unversty of Santa Catarna and the Engneerng and Knowledge Management Program for provdng the the opportunty to develop ths researh. Fundng Informaton Authors would lke to thank the Mnstry of Eduaton for provdng the fnanal support dretly to the Engneerng and Knowledge Management Program to whh the researh s lnked. Author s Contrbutons Beatrz Wlges: Man researher n the projet. Desgned the researh and developed the proposed solutons. Responsble for the wrtng of the majorty of the paper. Gustavo Mateus: Prepared the workflow for the experments, drew llustratons and also organzed data of the tables. Slva Nassar: Organzed the wrtng and struture of the manusrpt. Renato Cslagh: Responsble for edtng some parts of the paper and ommentng researh deas. Rogéro Cd Bastos: Researh advsor. Supervson and montorng of the researh. Eths Ths artle s orgnal and ontans unpublshed materals. The orrespondng author onfrms that all other authors have read and approved the manusrpt and there are no ethal ssues nvolved. Referenes Baker, L.D. and A.K. MCallum, Dstrbutonal lusterng of words for text lassfaton. Proeedngs of the 21st Annual Internatonal ACM SIGIR Conferene on Researh and Development n Informaton Retreval, Aug , Melbourne, Australa, pp: DOI: / Bang, S.L., J.D. Yang and H.J. Yang, Herarhal doument ategorzaton wth k-nn and oneptbased thesaur. Inform. Proess. Manage., 42: DOI: /j.pm Dumas, S. and H. Chen, Herarhal lassfaton of Web ontent. Proeedngs of the 23rd Annual Internatonal Conferene on Researh and Development n Informaton Retreval, Jul , Athens, Greee, pp: DOI: / Lews, D.D. and M. Rnguette, Comparson of two learnng algorthms for text ategorzaton. Proeedngs of the 3rd Annual Symposum on Doument Analyss and Informaton Retreval, (AIR 94), pp: Lews, D. D, Nave (Bayes) at forty: The ndependene assumpton n nformaton retreval. European onferene on mahne learnng. Sprnger Berln Hedelberg. Jang, J.Y., S.C. Tsa and S.J. Lee, FSKNN: Multlabel text ategorzaton based on fuzzy smlarty and k nearest neghbors. Expert Syst. Appl., 39: DOI: /j.eswa Wang, L.X., A Course n Fuzzy Systems and Control. 1st Edn., Prente Hall R, Upper Saddle Rver, ISBN-10: , pp: 424. MATLAB, Fuzzy log toolbox desgn and smulate fuzzy log systems. The MathWorks, In, MATLAB. Mannng, C.D., P. Raghavan and H. Shütze, Introduton to Informaton Retreval. 1st Edn., Cambrdge Unversty Press. ISBN-10:

9 Beatrz Wlges et al. / Journal of Computer Senes 2016, 12 (7): DOI: /jssp L, M., L. Lu and C.B. L, An approah to expert reommendaton based on fuzzy lngust method and fuzzy text lassfaton n knowledge management systems. Expert Syst. Appl. Int. J., 38: DOI: /j.eswa Peters, C.M.E.E. and C.H.A. Koster, Unertanty and term seleton n text ategorzaton. Int. J. Un. Fuzz. Knowl. Based Syst., 11: DOI: /S Qunlan, J.R., C4.5: Programs for Mahne Learnng. 1st Edn., Morgan Kaufmann, San Mateo, ISBN-10: , pp: 302. Qunlan, J.R., Induton of deson trees. Mahne Learn., 1: DOI: /A: RapdMner, Tan, S., An effetve refnement strategy for KNN text lassfer. Expert Syst. Appl. Int. J., 30: DOI: /j.eswa Wlges, B., G. Mateus, R. Bastos and M. Dantas, A ase-omparson study of automat doument lassfaton utlzng both seral and parallel approahes. J. Phys. Conf. Seres. Yagunuma, C.A., W.C. Magalhães Jr, M.T. Santos, H.A. Camargo and M. Reformat, Combnng fuzzy ontology reasonng and mamdan fuzzy nferene system wth HyFOM reasoner. Proeedngs of the Internatonal Conferene on Enterprse Informaton Systems, Jul. 4-7, Angers, Frane, pp: DOI: / _11 Yang, Y. and J. Pedersen, A omparatve study on feature seleton n text ategorzaton. Proeedngs of 14th Internatonal Conferene on Mahne Learnng, (CML 97), Morgan Kaufmann Publshers, San Franso, US pp: Yang, Y. and X. Lu, A re-examnaton of text ategorzaton methods. Proeedngs of the 22nd Annual Internatonal ACM SIGIR Conferene on Researh and Development n Informaton Retreval, Aug , Berkeley, CA, USA, pp: DOI: /