5] icadet 035

Classification of Data Streams Using Adaptiv e Naïve Bayes Algo rithm Machine Learning Classification Techniques: A Comparative Study Archana Chaudhary 1 , Savita Kolhe ! "a# Kamal $ 1&3 SCS & I ! DA""! # Soy$ean %esearch! ICA%' ()mail * archana+##,-re diffmail.com! savita+da/hane-y ahoo.com! dr+ra0/amal-hotmail %com Abstract – Machine learni ng is the stu dy of comput er algorithms that improve automatically &ith e'perience% (n other &ords it is the a)ility of the computer program to acquire or develop ne& *no&ledge or s*ills from e'amples for optimising the performance of a computer or a mo)ile device% (n this paper &e apply machine learning techniques +ayes net&or*, Logistic "egression, ecision Stump, -./, "andom 0or est, "andom Tree and "2tre e to )ui ld classifier models and compare them% (n this study machine learning techniques are applied to agriculture data and for each cl assi fi er model correctl y cl assi fi ed instances, inc orr ect ly classi fie d ins tances , model )ui ld time and *appa statisti cs are computed% The report ed test results descri )e the appl ica)il it y and ef fect iveness of the classification approach . Keywords – C4.5, Decision Stump, GSM, REtree, !G", #4$. I. I N%DUCIN Intel ligent syste ms lea rn $y imp rov ing thr o2g h epe ri ence 415. his lear ni ng pr ocess is not only re str ict ed to h2mans $2t spreads acr oss many fields incl2ding machine learning! psychology! ne2roscience! ed2c ation! comp2 tatio nal ling2 istics! economics and $ioinformatics he field of 6achine learning deals 7ith deve loping p rogr ams th at le arn f rom pa st data and is als o a $ra nch of dat a pro ce ssi ng. 6ac hine lea rni ng incl 2des the st re am in 7hic h ma chines lear n for /no7ledge gain or 2nderstanding of some concept or s/ill $y st2dying the instr2ction or from eperience 4#5. 6achine learning techni82es consist of form2lation of programs that imitate some of the facets of h2man mind that helps 2s to solve highly complicated pro$lems at a very good speed 435. h2s! machine learning has great potential in improving the efficiency and acc2racy of dec isi ons dra 7n $y int ell ige nt comp2ter pr ogr ams. 6achine learning incl2des mainly concept learning and classification learning. Classification is the most 7idely 2s ed 6a ch ine le ar ni ng te chni 82e that invo lves separating the data into different segments 7hich are non)overlapping. 9ence classification is the process of finding a set of models that descri$e and disting2ish class la$el of the data o$0ect 4:5. 6achine learning field is also 2sef2l for mo$ile devices s2ch as Smart phones! smartcards and sen sors! handheld and a2t omo tive comp2ting systems 4;5. 6o$ile e chnology has fostered deve lopment 7i th the he lp of incr ea si ng mo $i le terminals e.g . comp 2ters ! mo$i le comp 2ter s! mo$il e phones! <oc/et <C! <DA' and mo$ile net7or/s =S6! 3=>! 7ir eless net 7or/s ! Bl2etooth etc .'. 6ac hine learning techni82es li/e C;.:! Naïve Bayesian! Decision trees etc are he lpf2l for mo$i le de vices. 6achine learning applications for mo$ile devices incl2de Sensor $ased activity recognition! 6o$ile tet categori?ation! 6a l7ar e de tect ion on mo$ile de vi ce s! @ang2age 2nderstanding etc. II. 6(9DS A. Bayes Network Classfier Bay esia n net 7or/s are a po7er f2l pro $a$ ili sti c rep resen tat ion! and the y ar e 2se d for cla ssification p2rposes 4#5. Bayesian net7or/s are also called $elief net 7or/s and $el ong to the gr o2p of pro $a$ ilistic grap hical mode ls .he se grap hical str2 ct2r es are 2sed for /no7ledge representation of an 2ncertain domain. In this net7or /! ea ch node in the graph re pr esents a random varia$le! 7here as the edges $et7een the nodes re pr esent pr o$a$il isti c de pe ndenci es among the corre spo ndi ng ra ndom var ia$ les. hese conditional dependencies in the graph are often estimated $y 2sing /no7n sta tis tic al and comp2t ati ona l me thods. he Ba ye sian cl as si fi er lear ns fr om tr aini ng da ta the condi tiona l pro$a $ility of each attri$2te Bi give n the class la$ el 45 . Cla ssi ficati on is done $y app lyi ng Bayes r2le to calc2late the pro$a$ility of given the partic2lar instances of B1..Bn and then predicting the class that has highest posterior pro$a$ility. he naive <roceedings of International Conference on Information e chnology and 6anagement 21

5] icadet 035

Documents