Information Attribute Reduction Based on the Rough Set Theory

Transcription

1 Internatonal Journal of Engneerng and Appled Scences (IJEAS) ISSN: , Volume-3, Issue-11, November 2016 Informaton Attrbute Reducton Based on the Rough Set Theory YU Bo-wen, Du hao, Wang Zh-guo Abstract The genetc algorthm s used to optmze the algorthm of attrbute reducton n data preprocessng, and the rough approxmaton precson n the rough set theory s utlzed to determne the mportance of nformaton attrbute. From whch the decson table s consttuted by selectng the attrbutes whch have hgher degree of attrbute mportance, and the attrbute core of decson nformaton s obtaned by usng the dentfcaton matrx. The ntal populaton s constructed on the standard of the attrbute core, the search area of genetc algorthm s reduced. Fnally, the correcton operator based on the rough approxmaton precson s ntroduced, and the algorthm s made to conduct n the correct soluton space, thus the speed of attrbute reducton s mproved, furthermore, the optmal results of attrbute reducton are obtaned. Index Terms Genetc algorthm, The rough set, The rough approxmaton precson, Attrbute reducton. I. INTRODUTION Attrbute reducton s one of the man research drecton of rough set theory. Attrbute reducton s to keep the orgnal data classfcaton ablty under the premse of get rd of those who are not related to characterze the propertes. It has been proved the computaton of mnmal reducton and full reducton both s N-hard problem, nformaton of permutaton and combnaton s the mportant factor to the N - hard. For example, Guo-yn Wang, usng the subset was proposed to calculate the mnmalst attrbute reducton, the method of tme complexty s exponental, exponentally wth the ncrease of attrbutes ts complexty growth, so ths method s not sutable for practcal applcaton. Duo-Qan Mao proposed a reducton method based on nformaton entropy and nformaton entropy to defne attrbute mportance often cannot get the smallest reducton, s also lkely to get reducton results. For nformaton attrbute reducton, optmzaton algorthm s usually adopted, whch adds and sets standard nformaton accordng to the characterstcs of attrbute nformaton to reduce the search attrbute range to obtan the desred fnal result. Accordng to researches, the attrbute reducton n the rough set theory can be regarded as a combnaton optmzaton process, so the genetc algorthm can be ntroduced nto attrbute reducton. Genetc algorthm refers to a global search algorthm, and s featured by good stablty and parallel executon ablty. In the process of past research, a new heurstc genetc algorthm for producton schedulng s proposed by Jan-hua YU Bo-wen, Automatzaton Engneerng ollege, Nanjng Unversty of Scence and Technology, Nanjng, hna, DU hao, Second Team of Specal rocess,underground Operaton Branch of Daqng Olfeld ompany, Wang Zh-guo, lannng and Desgn Insttute, etrochna Daqng Olfeld No.9 roducton lant, da, select ndvdual state to establsh the ntal group by means of random, ths method requres a great deal of calculaton and evoluton algebra to search all state, ths algorthm under a large amount of nformaton effcency to be mproved. descrbe the mportance of attrbute rough approxmaton accuracy s proposed by Dong-y ye, accordng to the concept of rough approxmaton precson a greedy algorthm s desgned, t s haracterstcs s to realze the process s relatvely smple, can quckly fnd attrbute reducton n a large amount of condton attrbutes. In ths paper, the concept of attrbute sgnfcance s ntroduced on the bass of the concept of rough approxmaton accuracy, and attrbute nformaton of hgh attrbute sgnfcance among the orgnal attrbute nformaton s selected accordng to ths standard to form the ntal populaton of genetc algorthm, to narrow the search range of soluton space and rase the speed of searchng the optmal reducton results. At last, t s proved through experments that the genetc algorthm based on the rough set theory can greatly rase the accuracy of reducton results and reducton effcency. A. Rough Set II. BASI ONETS The rough set theory s a mathematc tool that allows varous nterferences such as naccurate analyss, dsaffnty and mcompleteness put forward by a olsh mathematcan named awlak.z n Through scholars unremttng efforts n studyng the propertes and laws of rough set n the past years, a substantve leap has been made n the rough set theory. Snce rough set s superor n data preprocessng, rough set has a good applcaton prospect n the feld of data mnng. Rough set s useful for standardzng and denosng data, processng mssng data, data reducng, and dentfyng correlaton. Rough set has been successfully appled n other related felds. Thus, the rough set theory s of great sgnfcance to the feld of data mnng. B. Genetc Algorthm In the 1960s, an Amercan professor Holland put forward a new theory. He had bult an artfcal ntellgence model by mtatng the bologcal evolutonsm base on Darwn s theory of survval of the fttest, and appled the dea of genetc varaton of organsms for adaptng themselves to changes n nature n the process of optmzaton. It s called genetc algorthm (GA). Wth research and development n recent years, great achevements of the applcaton of the genetc algorthm n other felds have been made. Genetc algorthm s a bonc algorthm, namely carryng out space optmzaton search by smulatng the process of organsms changng wth the envronment, performng genetc operaton over ndvduals va genetc operators, and formng a new 36

2 Informaton Attrbute Reducton Based on the Rough Set Theory evolutonary populaton wth evolved ndvduals.. Attrbute Sgnfcance Based on Rough Approxmaton Accuracy 1) Rough Approxmaton Accuracy Defnton of rough approxmaton accuracy: Suppose, X, X, X,..., X k s expressed by the decson attrbute of U, and X U, then the approxmaton accuracy of the attrbute set s a card X. card ( X ) refers to the number of card X X, based on whch the degree of attrbute reducton of the set can be determned. The smaller ts value s, the hgher the reducton degree s. The rough approxmaton progress of the attrbute set by L s: L 1 k card X. Defnton 1: The rough approxmaton accuracy of the attrbute set by L s: L 1 k card X. card X 2) Attrbute Sgnfcance Accordng to the defnton of rough approxmaton accuracy, t s put forward n ths paper to calculate attrbute sgnfcance based on rough approxmaton accuracy, to descrbe the sgnfcance level of attrbute nformaton. Suppose refers to basc attrbute, D to decson attrbute, contans n basc attrbutes 1, 2,..., n, L by the decson attrbute s expressed as x1, x2,..., x n, the rough approxmaton accuracy of each attrbute can be calculated out, and the expectaton and varance of the k+2 values can be calculated out based on the L rough approxmaton accuracy. S Defnton 2: D. Deducton and roof sgnfcance attrbute functon:, wheren refers to an auxlary parameter. Deductons 1 and 2 are obtaned accordng to the defnton of rough approxmaton accuracy: Deducton 1: Suppose a decson nformaton system S ( U, D, V, f ), then ts L s dvded accordng to U based on the decson attrbute D, so L 1. roof: Suppose U IND X, X, X,..., X / n, n whch n refers to the number of classfcaton of condtons of characterzaton by the doman of dscourse U. Accordng to the defntons above, L 1 k card X, n whch k refers to the statstc of the decson attrbute of the decson nformaton. In the decson nformaton table, the dvson of the doman of dscourse U by the condtons of characterzaton depends on the dvson of the doman of dscourse U by the decson attrbute D, thus IND IND D ; and the condtons of characterzaton dvdes the doman of dscourse U nto several classfcatons X, and clearly defnes t n L, so card( U ) card( X ) n 1. Accordng to the defnton of k card X L, 1 1 L. Deducton 2: The components modfed wth modfed OS D OS D. operators all comply wth roof: The stop condton of the modfcaton process s. Accordng to Deducton 1,. If 1, 2,..., r L L L L L L1, L2,..., Lk 1 and are equvalence relaton sets of and D, t can be obtaned that L 1 k card L rough approxmaton accuracy as above. Suppose accordng to the defnton of OS D OS D, then x x OS D, and d d U and x x L, x L U j x L 1. j 1 The result contradcts the proposton, then OS D OS D. III. GENETI ALGORITHM BASED ON ROUGH SET THEORY The genetc algorthm based on the rough set theory has preserved the basc characterstcs of the orgnal genetc algorthm; and s to select attrbutes at hgh sgnfcance level to form an nformaton decson system accordng to the attrbute sgnfcance of Defnton 2, calculate the core of attrbute reducton wth a dscernblty matrx, and determne the ntal populaton of the algorthm accordng to the core attrbute, to develop a strong search capablty of the algorthm wthn the local search space. Besdes, modfed operators based on rough approxmaton accuracy have ntroduced nto the algorthm to reduce the populaton, determne that every chromosome corresponds to a canddate reducton, and guarantee the algorthm s operated n a correct soluton space by the constrant of rough approxmaton accuracy. The algorthm n ths paper also has the overall optmzaton characterstcs of the orgnal algorthm, and has mproved the speed of attrbute reducton and reducton accuracy. The detals of the algorthm are as follows: Input: Informaton decson system S U, D, V, f, crossover probablty r, 37

3 Internatonal Journal of Engneerng and Appled Scences (IJEAS) ISSN: , Volume-3, Issue-11, November 2016 selectve probablty ; m, attrbute sgnfcance parameter Output: Attrbute reducton of ( D ); 1) alculate the rough approxmaton accuracy by the condton attrbute set : k card X L ; 2) alculate the sgnfcance 1 S of each characterzaton, sequence the calculatons, dentfy the attrbutes of whch the functon value of sgnfcance attrbute s large, and form S therewth. alculate the attrbute core of the decson system wth the dscernblty matrx, generate an ntal populaton, and determne the generaton number of the populaton, k=1; 3) alculate the ftness nformaton of every elements of the populaton accordng to the ftness functon preset n the system; 4) Judge whether the algorthm s termnated: If the result comples wth the termnaton end, the algorthm s termnated; If not, the selectve probablty of correspondng ndvduals should be calculated, so as to generate a new populaton, k=k+1; 5) erform crossover operaton over ndvduals; 6) erform mutaton operaton over ndvduals; 7) Modfy the result, determne that the attrbutes of whch the attrbute sgnfcance level s hgh are the search range of attrbutes, and return to the thrd step of the algorthm; The algorthm flow chart s as shown n Fg. 1: Start k=1 A. Modfed Operator IV. ALGORITHM ANALYSIS Reduce the populaton wth modfed operators, determne that every chromosome corresponds to a canddate reducton, guarantee the algorthm s operated n a correct soluton space by the constrant of rough approxmaton accuracy, and calculate the local optmal soluton based on the chromosomes n the k+1 optmzaton result. Accordng to Deducton 2, n modfcaton and verfcaton, the soluton space can be planned on the bass of rough approxmaton accuracy, whch should be specfcally determned by the correlaton between L and L R. Select a characterzaton of whch the rough approxmaton accuracy value s comparatvely larger from the characterzaton set not ncluded n the kth generaton of optmzaton result, and add t nto the planned search space, to make preparatons for gettng satsfactory attrbute reducton. The specfc flow chart s as shown n Fg. 2. Steps: 1) alculate the rough approxmaton accuracy of the exstng characterzaton set: L 1 k card X : If ( L) R( L) steps 2 and 3; If ( L) ( L) 2) Select the maxmum, turn to step 4; R S, repeat from the characterzaton set; 3) If the code poston n correspondence wth j s 0, change t nto 1, and turn to Step 1; 4) Modfcaton ends. alculate the rough approxmaton precson and ts correspondng attrbute mportance Intalze the populaton Ftness evaluaton Y Whether meet the condtons N End Select operaton rossover operaton Mutaton operaton Modfcaton of the newly generated chromosomes The next generaton of populaton k=k+1 Fg. 1 Flow hart of Improved Algorthm Fg. 2 Modfcaton Flow B. Settng of Intal opulaton Sze In the reducton process based on genetc algorthm n Lterature [9][10], the ntal populaton s not set. In practce, f the ntal populaton s approxmate to the problem soluton, the tme taken to solve algorthm can be saved, and t s easer to calculate the optmal soluton wth the algorthm. Therefore, the settng of the ntal populaton s of great mportance to the executon effcency of genetc algorthm. If the populaton sze s set to be large, the populaton can be restraned from premature convergence, whch complcates the executon of algorthm; f the populaton sze s set to be 38

4 Informaton Attrbute Reducton Based on the Rough Set Theory excessvely large, the optmal performance (reducton of system attrbutes) of the algorthm may declne. Thus, an deal populaton sze should be: m n p p H s 2 2,( s ( 1)(1 s) c ( )) Wheren, ( H) refers to the length of template, n to the number of coded message postons of ndvdual chromosome, ( H) to crossover probablty, and to selectve p probablty. s Suppose an nformaton system has ts own core, then n the dscernblty matrx, f there s a lne consstng of only one 1 (the other elements all are 0 ), t ndcates that the attrbute cannot be dstngushed, and s the attrbute core. Every core corresponds to only one decson nformaton table, and s n the reducton of ts correspondng decson nformaton table. For purpose of ths paper, attrbutes of whch the sgnfcance functon value s hgh wll be selected to form the ntal populaton of the algorthm, to further mprove the selecton qualty of the ntal populaton. The core attrbute codes n correspondence wth all chromosomes of the ntal populaton all are 1, and every ntal populaton contans the requred core attrbutes.. odng Bnary sequence s adopted to represent chromosome nformaton. Each code poston has ts own specfc attrbutes. 0 and 1 are used to dstngush whether the selected nformaton set contans an attrbute value or not. The ntal populaton should be set to contan these core attrbutes. For example, suppose the attrbute core of a { a, a, a,..., a } subject to 10 condton decson table { a, a, a } attrbutes s 1 2 6, then t s requred to select chromosomes of whch the codes are 11***1**** to form the ntal populaton. D. Ftness Functon The target value approxmaton, selecton of proper attrbute set, and global control ablty n algorthm all are determned by a ftness functon. The selecton of ftness functon determnes the convergence drecton of algorthm. Attrbute reducton s to obtan a mnmalst attrbute set and own the orgnal nformaton processng ablty. A reducton set should meet: mnmum number of attrbutes, and attrbute classfcaton ablty. Snce the rough approxmaton accuracy of attrbute s set as the crteron for determnng the ntal populaton n ths paper, t s not necessary to consder attrbute classfcaton ablty, and t only needs to consder the number of attrbutes. Hence, ftness functon s defned as: f ( x) card ( c) count ( x) card () c refers to the number of condton attrbutes; count () x to the number of condton attrbutes contaned n chromosome. The ftness functon of the mproved algorthm n ths paper s f ( x) card ( c) count ( x). Accordng to ths formula, the ftness functon equals to the dfference between the number of condton attrbutes and that of condton attrbutes contaned n chromosome. Snce rough approxmaton accuracy s ntroduced, t only needs to control the dfference between the sad numbers, whch smplfes the calculaton process of the algorthm. Snce the soluton space s normalzed by rough approxmaton accuracy, and relatvely mportant condton attrbutes are determned, wth algorthm executon, the smaller the number of condton attrbutes contaned n chromosome s, the closer the result s to the requred mnmalst reducton. Hence, the larger the dfference value between the number of condton attrbutes and that of condton attrbutes contaned n chromosome s, the closer the result s to the mnmalst reducton. E. Feasblty and erformance Analyss 1) Feasblty of Ftness Functon The ftness functon of the mproved algorthm n ths paper s f ( x) card ( c) count ( x). Accordng to ths formula, the ftness functon equals to the dfference between the number of condton attrbutes and that of condton attrbutes contaned n chromosome. Snce rough approxmaton accuracy s ntroduced, t only needs to control the dfference between the sad numbers, whch smplfes the calculaton process of the algorthm. Snce the soluton space s normalzed by rough approxmaton accuracy, and relatvely mportant condton attrbutes are determned, wth algorthm executon, the smaller the number of condton attrbutes contaned n chromosome s, the closer the result s to the requred mnmalst reducton. Hence, the larger the dfference value between the number of condton attrbutes and that of condton attrbutes contaned n chromosome s, the closer the result s to the mnmalst reducton. 2) Algorthm omplexty Analyss For the algorthm, the evolutonary drecton of the populaton are effectvely controlled wth rough approxmaton accuracy and ftness functon, so that the result gets closer and closer to the mnmalst reducton, and fnally reach the mnmalst reducton of the decsve system. If these condtons are not set, the soluton space range of the algorthm s 2 m ; suppose the number of attrbute cores of the decsve system s n, after rough approxmaton accuracy s taken as the nspraton nformaton, the soluton space s narrowed to 2 m h. It s thus clear that the ntroducton of nspraton nformaton nto the algorthm narrows the search space. 3) Algorthm onvergence Analyss Usually, the convergence of genetc algorthm s determned by the desgn, crossover probablty and mutaton probablty of ftness functon. Defnton 3: If X e s s the off-lne performance of mplementaton strategy s n the envronment e, then: e 1 T T e 1 e X s f t In the off-lne performance, f t best f 1, f 2,..., f t f t refers to, e e e e e the tth generaton of ftness functon relatve to the envronment e. For purpose of ths paper, f ( x) card ( c) count ( x ). Off-lne performance s used to descrbe the average value of the predefned ftness functon of the algorthm, wth whch the convergence of the algorthm can be measured. For buldng the ftness functon, the chromosome ndvduals are taken nto account n detal. 39

5 ftness ftness Snce attrbute core nformaton s ntroduced at the tme of buldng the ntal populaton, the ftness functon s desgned to equal to the dfference value between the total number of attrbute characterzatons and the number of reduced attrbute characterzatons. Besdes, a modfcaton strategy based on rough approxmaton accuracy s ntroduced n follow-up executon. omparng wth algorthm for whch no attrbute core nformaton s ntroduced, ths algorthm s helpful to approach the output result faster, to prevent the expectaton of each generaton of populaton from greatly changng, and then make ths algorthm have a better convergence. F. Termnaton ondtons Snce there are no defnte termnaton condtons or model for attrbute reducton, there s no defnte termnaton functon. Accordng to the actual attrbute reducton flow, f the ftness functon values of consecutve k generatons of populaton don t change, t can be regarded that the desred optmzaton result has been acheved, and then the algorthm executon can be termnated. A. Fgures and Tables V. INFORMATION REDUTION B Accordng to the decsve nformaton table gven n Lterature [8], as shown n Tab. 1, the reducton result s a, c, d and b, c, d. Accordng to the reducton algorthm n the Lterature[10], the attrbute reducton result of Tab. 1 s,,c,,,, b, c, d, ab, a b d,a c d, and whle the real mnmalst reducton of the decsve nformaton table s cd,. Thus, ab,,c and a, b, d are not correct reducton results, a, c, d andb, c, d are not the mnmalst reducton. Tab. 1 Lterature [10] Decson nformaton table U a b c d class Execute attrbute reducton of the decsve nformaton as shown n Tab.1 wth the algorthm put forward heren, the Internatonal Journal of Engneerng and Appled Scences (IJEAS) ISSN: , Volume-3, Issue-11, November 2016 fnal chromosome codes of the algorthm populaton all are 0011.Accordng to the ftness functon f ( x) card ( c) count ( x ), the correspondng ftness value s 2. The attrbutes n correspondence wth the code contents are c and d, and ther correspondng attrbute reducton s cd,. Fgs. 3 and 4 show the comparson between the reducton process of the contents of Tab. 1 wth the algorthm n ths paper and that wth the algorthm n Lterature [8]. For the reducton process, t s predefned that the number of the ntal populaton s 30, the crossover probablty s p 0.7, the mutaton probablty s r p 0.05, the attrbute sgnfcance parameter s 0.1, m and the termnaton condton s that the ftness functon values of several consecutve generatons of populaton don t change Evoluton algebra gs. 3 Genetc algorthm based on rough set Evoluton algebra Fgs. 4 Genetc algorthm of Lterature [8] Through comparson wth the algorthm of Lterature [8], correct fault attrbute reducton can be obtaned wth the algorthm of ths paper, whch verfes the performablty of the genetc algorthm of ths paper. For a same fault data decsve table, the ftness functon of the genetc algorthm based on rough set put forward heren roughly remans unchanged after the 19 th generaton, whle the ftness functon of the genetc algorthm of Lterature [10] tends to be stable at least after the 25 th generaton. It s also verfed that the algorthm of ths paper can greatly reduce the teratons of the algorthm tself, enhance the convergence of genetc algorthm, and qucken the reducton speed. Snce the concept of attrbute sgnfcance s ntroduced nto the algorthm of ths paper as the standard for selectng the ntal populaton, and the core attrbutes of whch the sgnfcance level s hgh are selected from t to form a specfc ndvdual populaton, then several data sets of whch F 40

6 Informaton Attrbute Reducton Based on the Rough Set Theory the core attrbutes largely dffer are selected from the UI data set to verfy that the effcency of mass data processng of the algorthm of the paper s relatvely hgh. It s predefned that the number of the ntal populaton s 30, the crossover probablty s 0.9, the mutaton probablty s p m 0.05 r, and the termnaton condton s that the ftness functon values of several consecutve generatons of populaton don t change. The comparson between the output result of the algorthm of ths paper and that of the algorthm of Lterature [11] and Lterature [12] s as shown n Tab. 2. Tab. 2 The expermental results compared The number of nstances Number of attrbutes The core attrbutes number Lterature [11] algorthm Lterature [12] algorthm The algorthm of ths paper The number of teratons Tme(s) The number of teratons Tme(s) The number of teratons Tme(s) Accordng to the comparson experment as above, the larger the amount of data nformaton s, the longer the tme taken to execute the tradtonal genetc algorthm s. Both the executon tme and teratons of the algorthm of ths paper are reduced. It s thus clear that the mproved algorthm can greatly rase the effcency of attrbute reducton wthout reducton n the global search ablty of the orgnal algorthm. For the genetc algorthm based on rough set, core attrbute s ntroduced, so that the ntal populaton s set to be close to the fnal reducton result, the search range s reduced, the local search ablty s enhanced, and the convergence speed s quckened whle the attrbute reducton accuracy s guaranteed. REFERENES [1] GUO Qng-ln, ZHENG Lng. A Novel Approach for Fault Dagnoss of Steam Turbne Unt Based on Fuzzy Rough Set Data Mnng Theory[J]. roceedngs of the SEE, 2007,27(8): [2] WANG Yng-yng, LUO Y, TU Guang-yu. Fault Dagnoss Method for Dstrbuton Networks Based on the Rough Sets and Decson Tree Theory[J]. Hgh Volt age Engneerng,2008,34(4): [3] L Quan, Zhou Xng she. Spacecraft Fault Dagnoss Technology Based on Measurement and ontrol Data Mnng[J]. omputer Measurement & ontrol,2011,19(3): [4] ZHENG Rao, WANG KuSheng, ZHANG BeKe, WANG Yun. A fault dagnoss method for complex processes based on Regflow graph and data mnng[j]. Journal of Bejng Unversty of hemcal Technology (Natural Scence),2013,40(5): [5] WANG Guo-Yn, YU Hong, YANG Da-hun. Decson Table Reducton based on ondtonal Informaton Entropy[J]. HIN ESE J. OM U TERS,2002,25(7): [6] MIAO Duo-Qan, HU Gu-Rong. A HEURISTI ALGORITHM FOR REDUTION OF KNOWLEDGE[J]. JOURNAL OF OMUTER RESEARH& DEVELOMENT,1999,36(6): [7] YIN Ln-Z, YANG hun-hua, WANG Xao-L, GUI We-Hua. An Incremental Algorthm for Attrbute Reducton Based on Labeled Dscernblty Matrx[J].ATA AUTOMATIA SINIA, 2014,40(3): [8] Da Janhua, L Yuanxang. Heurstc Genetc Algorthm for Reducton of Attrbutes n Rough Set Theory[J]. JOURNAL OF XI AN JIAOTONG UNIVERSITY,2002,36(12): [9] Ye Dongy, Huang u-we, Zhao Bn. A Greedy Algorthm for Attrbute Reducton n Rough Set[J].Systems Engneerng and Electroncs,2000,22(9): [10] YE Dong-y, HEN Zhao jong. An mproved reducton algorthm of attrbute n rough set[j].journal of Fuzhou Unversty(Natural Scence),2000,28(5):9-12. [11] REN Yong-gong, WANG Yang, YAN De-qn. Rough Set Attrbute Reducton Algorthm Based on GA.M INI- MIRO SYSTEM S,2006,27(5): [12] LI We-sheng, YI Zhe. Rough Set Attrbute Reducton Algorthm Based on GA[J]. M IROELETRONIS &OM UTER,2010,27: VI. ONLUSION In ths paper, the genetc algorthm based on rough set s detaled. The concept of attrbute sgnfcance s put forward based on the concept of rough approxmaton accuracy as the standard for selectng attrbute nformaton, and ntroduced nto the algorthm put forward n ths paper, to determne the core attrbute of attrbute reducton. It s proposed to determne the ntal populaton of genetc algorthm accordng to characterzaton attrbutes nformaton, so as to rase the executon effcency of the algorthm, and acheve a good convergence. A smple ftness functon s desgned. The ftness functon equals to the dfference between the total number of attrbutes and the number of the attrbutes obtaned va reducton. In ths way, the calculaton s smplfed. An attrbute sgnfcaton modfcaton strategy based on rough approxmaton accuracy s adopted, so that satsfactory solutons can be obtaned wthn the local space wth the algorthm, the reducton result contans fewer attrbutes and remans the classfcaton ablty as same as that of the orgnal data, search wthn the specfed feasble soluton space s guaranteed, and the convergence s guaranteed. At last, example analyses are made to verfy that the genetc algorthm based on rough set s effectve n solvng attrbute reducton. Frst Author Name: YU Bo-wen ublcatons : ommuncaton Module of F-AE-1553 Interface Improved Apror Algorthm Based on Matrx Reducton 41