Classifier Ensemble Design using Artificial Bee Colony based Feature Selection

Transcription

1 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 3, No 2, May 2012 ISSN (Onlne): Classfer Ensemble Desgn usng Artfcal Bee Colony based Feature Selecton Shunmugaprya Palansamy 1 and Kanman S 2 Research Scholar, Department of Computer Scence and Engneerng, Pondcherry Engneerng College, Puducherry, Inda Professor, Department of Informaton Technology, Pondcherry Engneerng College, Puducherry, Inda Abstract Artfcal Bee Colony (ABC) s a popular meta-heurstc search algorthm used n solvng numerous combnatoral optmzaton problems. Feature Selecton (FS) helps to speed up the process of classfcaton by extractng the relevant and useful nformaton from the dataset. FS s seen as an optmzaton problem because selectng the approprate feature subset s very mportant. Classfer Ensemble s the best soluton for the ptfall of accuracy lag n a sngle classfer. Ths paper proposes a novel hybrd algorthm ABCE the combnaton of ABC algorthm and a classfer ensemble (CE). A classfer ensemble consstng of Support Vector Machne (SVM), Decson Tree and Naïve Bayes, performs the task of classfcaton and ABCE s used as a feature selector to select the most nformatve features as well as to ncrease the overall classfcaton accuracy of the classfer ensemble. Ten UCI (Unversty of Calforna, Irvne) benchmark datasets have been used for the evaluaton of the proposed algorthm. Three ensembles ABC- CE, ABC-Baggng and ABC-Boostng have been constructed from the fnally selected feature subsets. From the expermental results, t can be seen that these ensembles have shown up to 12% ncrease n the classfcaton accuracy compared to the consttuent classfers and the standard ensembles Baggng, Boostng, ACO-Baggng and ACO-Boostng. Keywords: Feature Selecton, Classfcaton, Classfer Ensemble, Ant Colony Optmzaton, Bee Colony Optmzaton, Artfcal Bee Colony, Meta-heurstc search. 1. Introducton Ensemble Learnng has been a great topc of research durng the last decade and vast amount of works have been carred out n the doman of Classfer Ensemble (CE) [2] and [3]. Classfer Ensemble s the combnaton of two or more classfcaton algorthms and t s formed as a best soluton to overcome the lmtaton of accuracy lag of a sngle classfer. Baggng, Boostng, Stackng, Maorty Votng, Behavoral Knowledge Space and Wernecke s are some popular ensemble technques. When the consttuent classfers n CE are of same type, t s called homogeneous CE otherwse heterogeneous CE [2] and [3]. In CE, each consttuent classfer s traned over the entre feature space. Sometmes the feature space s nosy consstng of rrelevant and redundant data [1]. In such cases, the classfer consumes more tme to get traned and also ts msclassfcaton rates are hgher. Feature Selecton (FS) s a possble soluton to ths problem. Feature Selecton extracts the necessary and relevant data from the feature space wthout affectng the orgnalty of ts representaton. Wth FS, the performance of the classfer s mproved, thereby mprovng the effcency of the ensemble [10]. Feature Selecton has been wdely used n the constructon of ensembles [2]. Whle employng FS for ensemble constructon, results would be better when FS s optmzed. Swarm and evolutonary algorthms are used for optmzng feature selecton resultng n optmal feature subset. In lterature, Genetc Algorthm, Ant Colony Optmzaton, Bee Colony Optmzaton and Partcle Swarm Optmzaton are used n numerous applcatons for optmzng FS [8], [9], [17] and [18]. ABC s a stochastc, swarm ntellgent algorthm proposed by Karaboga Et. al. for constraned optmzaton problems [4]. Snce ts proposal, ABC has been proved to be successful n solvng optmzaton problems n numerous applcaton domans. Also ABC s proved to gve promsng and enhanced results n the areas where Genetc Algorthm and ant Colony Optmzaton have gven already [4], [5], [6] and [7]. In order to enhance the classfcaton accuracy, dfferent algorthms for pattern classfcaton [1], dfferent technques for feature selecton and a number of classfer ensemble methodologes [2] and [3] have been Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

2 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 3, No 2, May 2012 ISSN (Onlne): proposed and mplemented so far. The man lmtaton of these methods s that, none of them could gve a consstent performance over all the datasets [8]. The proposed method s also attempted as an effort towards effcent feature selecton optmzaton and ensemble constructon. In ths study, classfer ensembles are constructed usng optmal feature subset obtaned from the combnaton of classfer ensemble and Artfcal Bee Colony Algorthm (ABC). ABC s used to select the features and generate the feature subsets and these feature subsets are evaluated for effcency by an ensemble made up of classfers Decson Tree (DT), Naïve Bayes (NB) and Support Vector Machne (SVM). Each tme ABC generates dfferent feature subsets, the CE uses the average of mean accuracy of the ensemble and consensus as the ftness measure to select the feature subset. Ths paper s organzed n sx sectons. Secton 2 descrbes about feature selecton and ts types. In Secton 3, a bref descrpton of Artfcal Bee Colony s presented. Secton 4 outlnes the proposed method ABCE: the ABC based feature selecton and the ensemble constructon are explaned n ths secton. The experments and results are dscussed n secton 5 and the paper s concluded n secton FEATURE SELECTION (FS) Feature selecton s vewed as an mportant preprocessng step for dfferent tasks of data mnng especally pattern classfcaton [10], [11] and [12]. When the dmensonalty of the feature space s very hgh, FS s used to extract the nformatve features from the feature space and the unnformatve ones wll be removed. Otherwse the unnformatve features tend to ncrease the complexty of computaton by ntroducng nosy and redundant data nto the process. Wth FS, the features are ranked based on ther mportance makng the feature set more sutable for classfcaton wthout affectng the orgnal feature representaton and accuracy of predcton [10]. It has been proved n the lterature that classfcatons done wth feature subsets obtaned by FS have hgher predcton accuracy than classfcatons carred out wthout FS [19]. A number of algorthms have been proposed to mplement FS. FS algorthms related to pattern classfcaton fall nto two categores: Flter Approach and Wrapper Approach. When the process of FS s ndependent of any learnng algorthm, t s called flter approach. It depends on the general characterstcs of the tranng data and uses the measures such as dstance, nformaton, dependency and consstency to evaluate the feature subsets selected [10] and [20]. On the other hand when a classfer s nvolved, t s called wrapper approach. The feature subset that results from a wrapper approach depends on the classfcaton method used and two dfferent classfers can lead to two dfferent feature subsets. Compared to flter approach the feature subsets obtaned through wrapper are always effectve subsets but t s a tme consumng process [20] and [24]. Independent of flter and wrapper approaches, evolutonary algorthms are also used for searchng the best subset of features through the entre feature space [8], [9], [17] and [18]. 3. The Artfcal Bee Colony Algorthm (ABC) ABC s a swarm ntellgent and meta-heurstc search algorthm proposed by Karaboga [4] and snce then t has been used wdely n many felds for solvng optmzaton problems [5], [6], [7], [17] and [18]. ABC s nspred by the foragng behavor of honey bee swarms. The ABC algorthm employs three types of bees n the colony: employed bees, onlooker bees and scout bees. Intally, the food source postons are generated (N) and the populaton of employers s equal to the number of food sources. Each food source represents a soluton of the optmzaton problem. Each employed bee s assgned a food source and they explot the food sources and pass the nformaton of nectar content to the onlookers. The number of onlookers s equal to the number of employed bees. Based on the nformaton ganed, the onlookers explot the food sources and ts neghborhood untl the food sources become exhausted. The employed bee of exhausted food sources becomes a scout. Scouts then start searchng for new food source postons. The nectar nformaton represents the qualty of the soluton avalable from the food source. Increased amount of nectar ncreases the probablty of selecton of a partcular food source by the onlookers [5]. The ABC algorthm s gven n Fg.1 [4]. 1. Intalze the food source postons 2. Evaluate the food sources 3. Produce new food sources(solutons) for the employed bees 4. Apply greedy selecton 5. Calculate the ftness and probablty values 6. Produce new food sources for onlookers 7. Apply greedy selecton 8. Determne the food source to be abandoned and allocate ts employed bee as a Scout for searchng the new food sources 9. Memorze the best food source found 10. Repeat steps 3-9 for a pre-determned number of teratons Fg.1 Steps of the ABC algorthm Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

3 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 3, No 2, May 2012 ISSN (Onlne): The Artfcal Bee Classfer Ensemble (ABCE) In the proposed study, the ensemble s constructed as a combnaton of ABC algorthm and a CE consstng of the classfers Decson tree, Naïve Bayes and Support Vector Machne [1] and [16]. In ABCE, ABC algorthm s used as a feature selector and feature subset generator; the ensemble classfer s used as the evaluator to evaluate the feature subsets generated. The classfer ensemble helps ABC n pckng up the best feature subset by evaluatng each confguraton suggested by ABC. The ABC algorthm helps n effcent CE constructon suggestng the best feature subset for the ensemble to work wth. Hence both ABC and CE try to enhance the performance of each other n the proposed method. The steps of ABCE are gven n Fg ABC Feature Selector The ABC algorthm s used to optmze the process of feature selecton and ncreases the predctve accuracy of the classfer ensemble. Frst, the classfer ensemble (made up of DT, SVM and NB) s used to evaluate the dscrmnatng ablty of each feature F n the dataset. Then, the ensemble accuracy ( x ) of each feature F s calculated by employng 10-fold cross valdaton [2] and [3] for each of the classfer. Each employed bee s assgned a bnary bt strng made of 0 s and 1 s. The length of the bnary bt strng s equal to the number of features n the dataset and s used to represent the feature selecton by each employed bee. A 1 means the feature s selected and a 0 means the feature s not selected. The populaton of the employed bees and onlooker bees are equal to the feature sze (m) of the dataset as features are consdered as food sources here. 1.Cycle =1 2. Intalze ABC parameters 3. Evaluate the ftness of each ndvdual feature 4. Repeat 5. Construct solutons by the employed bees For form 1 to m Assgn feature subset confguratons (bnary bt strng) to each employed bee Produce new feature subsets Pass the produced feature subset to the Classfer Ensemble v Evaluate the ftness ( ft ) of the feature subset based on the ensemble s mean accuracy and consensus Calculate the probablty p of feature subset soluton 6. Construct solutons by the onlookers For form 1 to m For form 1 to m Select a feature based on the probablty p Compute v usng x and Apply greedy selecton between v and x 7. Determne the scout bee and the abandoned soluton 8. Calculate the best feature subset of the cycle 9. Memorze the best optmal feature subset 10. Cycle = Cycle Untl pre-determned number of cycles s reached 12. Employ the same searchng procedure of bees to generate the optmal feature subset confguratons 13. Construct the ensembles ABCE, ABC-Baggng and ABC- Boostng usng the best optmal feature subset Fg. 2 Steps of ABCE Each employed bee s allocated a feature and t evaluates the ftness of the feature by usng the mean accuracy of the ensemble and the consensus [8]. The ftness for each feature (feature subset) ponted by the employed bee s calculated usng the equatons (1), (2) and (3). ftness 1 s) m = = 1 m x accuracy ( s) ( (1) ftness ( s) = consensus( ) (2) 2 s ftness1( s) + ftness 2( s) ft = (3) 2 accuracy (s) s the predctve accuracy of the th classfer n the ensemble and consensus(s) specfy the classfcaton accuracy usng consensus upon the s th feature subset [8]. The frst part of the ftness (mean accuracy) checks whether the feature subset has superor power on accurate classfcaton wth the whole classfer ensemble and targets to optmze t. So, the mean accuracy helps n ncreasng the generalzaton ablty of the feature subset. The second part of the ftness (consensus) checks for the optmalty of the feature subset n producng hgh consensus classfcaton [8]. Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

4 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 3, No 2, May 2012 ISSN (Onlne): The onlooker bee gans nformaton from the employed bee and calculates the probablty of selectng a feature usng equaton (4). Then the onlooker computes the new soluton v usng the ensemble accuraces of the feature the employed bee s pontng to and the feature the onlooker bee has selected. If the new soluton v s greater than x, the employed bee wll be pontng to feature subset consstng of the feature t was prevously pontng and the newly selected feature. If v s not greater than x then, the employed bees feature wll be retaned and the newly selected feature s neglected. The new soluton v s computed by usng equaton (5). p = m = 1 ft ft ( (4) v = x + ϕ x x ) (5) where, x s the ensemble accuracy of the feature allocated to the employed bee and x s the ensemble accuracy of the feature the onlooker has selected. ϕ s an unformly dstrbuted real random number n the range [0,1]. Ths way, each tme the employed bee s assgned a new feature subset, the onlooker explots and tres to produce new feature subset confguraton. After all possble features are exploted for formng the feature subset, the nectar content gets accumulated towards better feature subset confguraton. If any employed bee has not mproved, then the employed bee becomes a scout. The scout s assgned a new bnary feature set based on the equaton (6). x = x + rand 0,1]( x x ) (6) mn Where [ max mn and represents the lower and upper bounds of the dmenson of the populaton. The bees keep executng the same procedure for a pre-determned number of runs to form the best feature subset. Hence ABC s used to select and rank dfferent features based on ther mportance. So, relevant features are extracted and the computaton complexty due to rrelevant and nosy features s greatly reduced. Apart from ths, for large datasets especally wth large number of features, the performance of classfers s affected because t has to handle more number of features. By usng ABC, the number of features s scaled down based on ther mportance and the computatonal speed of the classfer s ncreased. 4.2 The Ensemble Classfer The classfers Decson Tree, Naïve Bayes and SVM are put together to form the classfer ensemble n the proposed method. In the proposed study, when the bees keep executng ther searchng procedure, the feature subset selected by each of the bees s nput to the classfer ensemble. The three classfers consder the canddate feature subsets one at a tme, get traned wth the combnaton of features and classfy the test set. After the classfers have fnshed, the ABC algorthm calculates the mean accuracy of the classfer ensemble and consensus usng equatons (1) and (2). The ftness s then calculated as the average of mean accuracy and consensus. The ftness (ft ) s used as the evaluaton crteron for selectng the best feature subset combnaton. In the proposed method, the classfer ensembles ABC- CE, ABC-Baggng and ABC-Boostng are constructed usng the fnally selected feature subset. ABC-CE s formed by the maorty vote [2] of the three classfers, Decson Tree, Naïve Bayes and SVM. Baggng [2], [3] & [13] and Boostng are most famous CE methods whch have been used n numerous Pattern Classfcaton domans [2], [3] & [14]. ABC-Baggng s constructed by the combnaton of C4.5 Baggng wth ABC selected feature subset. ACO-Boostng s constructed by Boostng the C4.5 decson tree along wth ABC selected feature subset. 5. Experments and Results The datasets used, the mplementaton and the results of AC-ABC are dscussed n ths secton. 5.1 Datasets The performance of the proposed method ABCE dscussed n ths study has been mplemented and tested usng 10 dfferent medcal datasets. Heart-C, Dermatology, Hepatts, Lung Cancer, Pma Indan Dabetes, Irs, Wsconsn Breast Cancer, Lmphography, Dabetes and Stalog-Heart are the datasets used. These datasets are taken from UCI machne learnng repostory Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

5 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 3, No 2, May 2012 ISSN (Onlne): [15] and ther descrpton s gven n Table I. The reasons for selectng these datasets are that they have been used n numerous classfer ensemble and feature selecton proposals for expermental proof. The datasets are chosen such that the number of features s n a vared range and large number of nstances, so that the effect of feature selecton by ABCE s easly vsble. 5.2 Implementaton of ABCE Table 1: Datasets Descrpton Dataset Instances Features Classes Heart-C Dermatology Hepatts Lung Cancer Pma Irs Wsconsn Lymph Dabetes Heart-Stalog Classfcatons of the datasets are mplemented usng WEKA Software from Wakato Unversty [16] and feature selecton usng ABC has been mplemented usng Net Beans IDE. Decson Tree s mplemented by usng J48 algorthm, SVM by the LIBSVM package and Naïve Bayes by the Naïve Bayes classfcaton algorthm from WEKA. The artfcal bees search for the best feature subset confguraton wth the followng parameter ntalzatons for ABC: Populaton Sze p : 2 * No. of features n the data set Dmenson of the populaton : p N Lower Bound : 1 Upper Bound : N Maxmum Number of teraton : Equal to the number of features No. of runs : 10 ϕ : 0.3 Wth these parameter settngs, the best optmal feature subset s recorded after executng a specfed number of cycles. After every teraton, the employed bees pass the selected features to the classfer ensemble for evaluaton. The mean accuracy of the classfers n the ensemble and the consensus upon the feature subset are calculated usng equatons (1) and (2). The ftness measure for each feature subset s the average of the mean accuracy and the consensus and t s calculated usng equaton (3). The onlookers decde upon a feature subset wth a probablty whch depends on the ftness. The number of features selected and the ensemble accuracy of ABCE s gven n Table 2. Table 2: Feature Selecton and Ensemble Accuracy Acheved through ABCE Dataset No. of Features Features Selected by Predctve Accuracy (ABCE)(%) ABCE Heart-C Dermatology Hepatts Lung Cancer Pma Irs Wsconsn Lymph Dabetes Heart-Stalog Three classfer ensembles ABC-CE, ABC-Baggng, ABC-Boostng are then constructed usng the optmal feature subset selected by the proposed ABCE method. The classfcaton accuraces acheved by these three ensembles are gven n Table 3. Also n Table 3, the performance of ABCE s compared wth ACO based ensemble, Baggng C4.5 and Boostng C fold Cross Valdaton has been used to evaluate the accuracy of the constructed ensembles [1], [2] and [3]. When the ABCE method s appled to the datasets and the ensembles are constructed usng the features output by ABCE, the recognton rates for all the ten datasets are mproved sgnfcantly and ths s shown n Fg.4. From the data represented n Table 2, Table 3 and Fg. 3, t can be nferred that:. Feature selecton defntely ncreases the classfcaton accuracy and speeds up the process of classfcaton. For all datasets except Hepatts and Dabetes, ABCE has gven the hghest recognton rates. For Hepatts, Boostng has gven the hghest accuracy and ABCE has gven better performance compared to ACO v. For dabetes, ACO has the leadng performance and accuracy of ABCE s margnally low compared to ACO v. For Heart-c, Irs, Pma and Wsconsn, feature subset obtaned s almost of same sze as n ACO v. For Lung Cancer, Lymph and Stalog, sze of the feature set s mnmzed to a greater level wth good predcton accuraces. Ths very well explans the effectveness of the proposed method v. Convergence of the search space s acheved quckly Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

6 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 3, No 2, May 2012 ISSN (Onlne): Table 3: Classfcaton Accuracy of the Ensembles by 10 Fold Cross Valdaton Baggng Boostng ACO - ACO- ABC- ABC- Dataset (C4.5) (C4.5) Baggng Boostng ABC -CE Baggng Boostng Heart-C Dermatology Hepatts Lung Cancer Pma Irs Lymph Wsconsn Dabetes Heart Stalog (All numbers are n percentage) Fg.3 Graph Showng the Comparson of Predctons for the Ten UCI Datasets by the Consttuent Classfers, Tradtonal Ensembles and Ensembles Constructed Usng ACO and ABCE Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

7 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 3, No 2, May 2012 ISSN (Onlne): v. In the graphcal representaton, the curve tends to elevate on the ABCE methods for most of the datasets, whch shows the betterment of the proposed method 6. Concluson In ths paper, a new method of classfer ensemble ABCE has been proposed and mplemented. ABCE s proposed by combnng the mult-obectve ABC wth a Classfer Ensemble (CE) and has been used to optmze the feature selecton process. Ths method has resulted n optmal selecton of feature subsets and the effectveness of the proposed method can be seen from the results obtaned. The ensembles ABC-CE, ABC-Baggng and ABC- Boostng developed usng the selected feature subset, has gven classfcaton accuraces ncreased by 12% than the consttuent classfers and the ensembles Baggng, Boostng, ACO-Baggng and ACO-Boostng. References [1] R.O. Duda, P.E. Hart and D.G. Stork, Pattern Recognton, John Wley & Sons, Inc 2nd edton, [2] L.I.Kuncheva, Combnng Pattern Classfers, Methods and Algorthms, Wley Interscence, [3] Polkar R., Ensemble based Systems n decson makng IEEE Crcuts and Systems Mag., vol. 6, no. 3, pp , [4] Karaboga and B. Basturk, A Powerful and Effcent Algorthm for Numercal Functon Optmzaton: Artfcal Bee Colony (ABC) Algorthm, Journal of Global Optmzaton, Sprnger Netherlands, vol.39, no.3, 2007, pp [5] Karaboga, An Idea Based on Honey Bee Swarm for Numercal Optmzaton, Techncal Report- TR06, Ercyes Unversty, Engneerng Faculty, Computer Engneerng Department, [6] D. Karaboga, B. Basturk, Artfcal Bee Colony (ABC) Optmzaton Algorthm for Solvng Constraned Optmzaton Problems, LNCS: Advances n Soft Computng: Foundatons of Fuzzy Logc and Soft Computng, Sprnger- Verlag, vol. 4529/2007, pp [7] Fe Kang, June L, Haon L, Zhenyue Ma and Qng Xu, An Improved Artfcal Bee Colony Algorthm, Proc. IEEE Internatonal Workshop on Intellgent Systems and Applcatons, 2010, pp 1-4. [8] Zl Zhang and Pengy Yang, An Ensemble of Classfers wth Genetc Algorthm Based Feature Selecton, IEEE Intellgent Informatcs Bulletn, 2008, Vol 9, No.1, pp [9] Nada Abd-Alsabour and Marcus Randall, Feature Selecton for Classfcaton Usng an Ant Colony System, Proc. Sxth IEEE Internatonal Conference on e Scence Workshops, 2010, pp [10] Dash.M and Lu.H., Feature Selecton for Classfcaton, Intellgent Data Analyss, Vol.39, 1997, No. 1, pp [11] Wtten, I. H., & Frank, E. (2005). Data mnng: Practcal machne learnng tools and technques. San Francsco: Morgan Kaufmann. [12] Han, J., and Kamber, M.: Data mnng concepts and technques, Academc Press, [13] L. Breman, Baggng predctors, Machne Learnng, 1996, vol. 24, no. 2, pp [14] Y. Freund, R.E. Schapre, Experments wth a new boostng algorthm, Proceedng of the Thrteenth Internatonal conference on Machne Learnng, 1996, [15] A.Frank, A. Asuncon, UCI Machne Learnng Repostory, ( Irvne, CA: Unversty of Calforna, School of Informaton and Computer Scence (2010)) [16] WEKA: A Java Machne Learnng Package, ml/weka/. [17] N.Suguna and K.G.Thanushkod, A novel Rough Set Reduct Algorthm for Medcal Doman based on Bee Colony Optmzaton, Journal of Computng, Vol. 2(6), 2010, [18] N.Suguna and K.G.Thanushkod, An Independent Rough Set Approach Hybrd wth Artfcal Bee Colony Algorthm for Dmensonalty Reducton, Amercan Journal of Appled Scences 8 (3), 2011, pp [19] Laura E A Santana, Lga Slva Anne M P Canuto, Fernando Pntro and Karlane O Vale, A Comparatve Analyss of Genetc Algorthm and Ant Colony Optmzaton to Select Attrbutes for an Heterogeneous Ensemble of Classfers, Proc. IEEE Congress on Evolutonary Computaton, 2010, pp [20] ShXn Yu, Feature Selecton and Classfer Ensembles: A Study on Hyperspectral Remote Sensng Data, Ph.D. Thess, The Unversty of Antwerp, P.Shunmugaprya receved her M.E. degree n 2006 from Department of Computer Scence and Engneerng, FEAT, Annamala Unversty, Chdambaram. She had been workng as a Senor Lecturer for the past 7 years n the Department of Computer Scence and Engneerng, Sr Manakula Vnayagar Engneerng College, Afflated to Pondcherry Unversty, Puducherry. Currently she s workng towards her Ph.D degree n Optmal Desgn of Classfer Ensembles usng Swarm Intellgent, Meta-Heurstc Search Algorthms. Her areas of nterest are Artfcal Intellgence, Ontology based Software Engneerng, Classfer Ensembles and Swarm Intellgence. Dr. S. Kanman receved her B.E and M.E n Computer Scence and Engneerng from Bharathyar Unversty and Ph.D n Anna Unversty, Chenna. She had been the faculty of Department of Computer Scence and Engneerng, Pondcherry Engneerng College from 1992 onwards. Presently she s a Professor n the Department of Informaton Technology, Pondcherry Engneerng College, Puducherry. Her research nterests are Software Engneerng, Software testng, Obect orented system, and Data Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.

8 IJCSI Internatonal Journal of Computer Scence Issues, Vol. 9, Issue 3, No 2, May 2012 ISSN (Onlne): Mnng. She s Member of Computer Socety of Inda, ISTE and Insttute of Engneers, Inda. She has publshed about 50 papers n varous nternatonal conferences and ournals. Copyrght (c) 2012 Internatonal Journal of Computer Scence Issues. All Rghts Reserved.