Ant Colony Optimization to Discover the Concealed Pattern in the Recruitment Process of an Industry

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1 N. Svaram et. al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng Ant Colony Optmzaton to Dscover the Concealed Pattern n the Recrutment Process of an Industry N. Svaram Research Scholar, Department of CSE Natonal Engneerng College Kovlpatt, Inda Svaram.Natarajan@artelmal.n K. Ramar M. Janak Meena Department of CSE Department of CSE Natonal Engneerng College PSG College of Technology Kovlpatt, Inda Combatore, Inda kramar_nec@redffmal.com mjanakmeena@yahoo.com Abstract Recrutment of the most approprate employees and ther retenton are the mmense challenges for the HR department of most of the ndustres. Every year IT companes recrut fresh graduates through ther campus selecton programs. Usually ndustres examne the sklls of the canddate by conductng tests, group dscusson and number of ntervews. Ths process requres enormous amount of effort and nvestment. Durng each phase of the recrutment process, canddates are fltered based on some performance crtera. The recrutment process of an ndustry dffers each year based on ther requrement trats and the process and crtera changes among the ndustres. Ths research focuses on nvestgatng the underlyng crtera and tres to captalze on the exstng patterns, to mnmze the effort made durng the recrutment process. Knowledge about the recrutment process was collected from the doman experts and decson trees were constructed wth t to dentfy superor selecton crtera. Most of the machne learnng algorthms ncludng decson trees have tanted performance n hgh dmensonal feature space and substantate sgnfcant ncrease n performance wth selected features. In ths paper, a novel technque based on Ant Colony Optmzaton s proposed to dentfy the attrbutes that mpacts the selecton process. The proposed ACO technque assgns heurstc nformaton for the attrbutes based on the estmated condtonal probabltes. Experments were carred out usng the dataset collected from an ndustry to dentfy the feature sets that gve greater accuracy. Decson trees constructed usng the C4.5 algorthm wth the set of attrbutes that nfluence the recrutment process were used to extract feasble rules after makng dscussons wth the doman experts. Keywords- Machne learnng, feature selecton, Ant colony optmzaton, C4.5 algorthm, decson trees I. INTRODUCTION Human resource s one of the back bones for ndustres to mantan ther compettve advantages n the knowledge economy. Selectng fresh people wth hgh talent and potental retenton s challengng and dauntng task faced by any HR department. The demand s ncreasng year over year and the supply has also been on the rase. But, the problem s wth the qualty n the supply for ts recrutment. Dfferent companes are fghtng t out by takng the frst slot, optng for dual placements, addressng the gaps by faculty development programs, etc. Campus recrutment s the predomnant mode of recrutment for fresh talented graduates. Because of the nconsstency n the qualty of the students produced by dfferent unverstes and the type of skll set they acqure durng ther program, selectng the rght canddate among those who graduate becomes a herculean task. Ths nvolves lot of effort by the recrutng team and money spent for the process s phenomenal. One of the mechansms used by the ndustres s to conduct tests and group dscussons durng the fltraton process. The selecton process uses dfferent crtera whch nclude the average of ther semester marks, marks obtaned n the apttude, programmng and techncal tests conducted by the company, group dscusson, techncal and HR ntervews. These crtera are common for all the students, but the skll level of the students vary snce they are from dfferent dscplnes and backgrounds. The tme taken and expendture for conductng group dscussons and ntervews consumes more than 90% of the total effort of the recrutment process. It has been observed that 1 among 120 students who apply get selected and the rato of number of canddates selected aganst the number of canddates ntervewed after tests s approxmately 1:20. Reducng these ratos wll mmensely help the ndustres to save the effort. Ths research focuses on determnng a set of selecton crtera to be appled to flter canddates based on ther background and academc data. Decson tree s a smple data mnng approach used to establsh the hdden knowledge n the data for classfcaton and predcton. They have the advantage of easy nterpretaton and understandng for the decson makers to compare wth ther doman knowledge for valdaton and justfy ther decsons [7]. Knowledge requred for the analyss was collected from an ndustry and attrbutes assocated wth the recrutment process were dentfed. There were totally fortyfour attrbutes. The structured dataset was splt nto tranng and testng set and decson trees were constructed wth tranng data and tested wthout overfttng. Experments were carred out wth dfferent subset of attrbutes and t was observed that the accuracy of the classfer ncreases wth decson trees constructed usng a few subsets of the doman attrbutes. Hence a feature selecton algorthm based on ACO s proposed to dentfy the rght set of attrbutes that mpact the selecton process and rules were extracted from decson trees constructed wth those attrbutes. ISSN :

2 N. Svaram et. al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng In machne learnng and statstcs, feature selecton, s the technque of selectng a subset of relevant features for buldng robust learnng models. Ant Colony Optmzaton s a metaheurstc approach that could be appled for feature selecton. Characterstcs of the ACO nclude postve feedback, dstrbuted computaton and constructve greedy heurstc. Ant Colony Optmzaton s a technque nspred by the observaton of real ants n ther search for shortest paths to food source. The ants communcate through a chemcal aromatc substance called as pheromones [1], [6]. In ths model, subset selecton problem s reformulated as a search problem n a connected graph. The rest of ths paper s organzed as follows: Secton 2 presents a bref overvew of the methodologes used n the proposed algorthm; Secton 3 descrbes the proposed algorthm and Secton 4 descrbes the dataset and about the expermentatons carred out and results. II. BACKGROUND KNOWLEDGE In ths secton, the general procedure of recrutment process, the process of feature selecton, decson tree algorthm and Ant colony optmzaton algorthm are explaned. A. Recrutment Process Selectng the rght persons for the rght job s the most mportant challenge n the human resource management. The varous selecton methods nclude analyss of applcaton form, self-assessment, telephone screenng, tests dependng on the requrement of the ndustry (such as apttude, techncal, programmng, personalty, nterest test, etc.) [13], [14]. Generally ndustres use a combnaton of the selecton methods, based on ther job nature, cost, tme, accuracy, culture and acceptablty. Accordng to Lews, there are three aspects of selecton crtera. They are organzatonal crtera, functonal/departmental crtera and ndvdual job crtera. Fnally, the recrutment commttee must consder the adaptaton of the job, departmental and organzatonal characterstcs to the applcant s characterstcs [12], [8]. Hence the recrutment commttee desgns each level of the recrutment process to reflect ther needs. The recrutment process n the campus ntervews of an IT ndustry ncludes flterng based on ther semester marks n the graduaton, marks obtaned n the apttude, techncal and programmng tests conducted by the ndustry, grade obtaned durng group dscusson and ntervews. The company prepares a set of questons to test, f the canddate s really capable of applyng what he/she learnt n hs/her course of study. The questons also map to the expectatons and job descrpton for whch he s recruted. To check the presentaton, communcaton and behavoral sklls of the canddate a group dscusson s conducted. B. General Procedure for Feature Selecton Attrbutes of the tranng data are the nodes n the search space and the search s made to determne the optmal subset. The procedure of feature selecton conssts of four steps subset generaton, subset evaluaton, stoppng crtera and result valdaton [2], [3]. Subset Generaton Subset generaton s a process of heurstc search n the search space, to dentfy a subset S. The dsposton of ths process s determned by two ssues: the startng pont of the search procedure and search strategy. Search procedure may start wth an empty set and add features consecutvely (forward approach) or start wth all the features n the subset and elmnate one at a tme n each step (backward approach) or start wth an empty set and a full set, add and remove feature smultaneously n each step. Search may also begn wth a randomly selected subset n order to avod gettng trapped n the local optma. Exstng search strateges nclude complete search, sequental search and random search [2]. Generally exhaustve search s a complete search, n whch every possble subset s analyzed, but the complexty of the search s exponental to the cardnalty of feature space. Sequental search algorthms nclude ped greedy search algorthm, such as sequental forward selecton, sequental backward selecton, and bdrectonal selecton. Sequental search gves up completeness, thus work at rsk to lose the optmal solutons. Random search starts wth randomly selected subsets and may be proceed n a sequental way as random-restart hll clmbng algorthm or generate next subset n a completely random manner. Randomness helps these methods to escape the local optma [2]. Subset Evaluaton Each newly generated subset s evaluated by a classfer dependent or ndependent evaluaton crteron. Classfer ndependent algorthms are used n flter model and the goodness of the selected subset s exploted by the ntrnsc characterstcs of the tranng data. Some popular ndependent evaluaton crtera are dstance measures, dependency measures, and consstency measures. A dependant crteron used n the wrapper model requres a predetermned mnng algorthm, the performance of the algorthm s consdered as the evaluaton crtera. Performance of wrapper model s relatvely hgh when compared to flter model, but the complexty of the model s very hgh [2], [3]. The proposed algorthm uses wrapper model of feature selecton and accuracy of the decson tree classfers s used to evaluate the selected subset. Stoppng Crtera Stoppng crtera of the algorthm determnes when the algorthm should stop. The stoppng crtera may be formulated based on one of the followng:. The number of teratons.. When subsequent addton or deleton of features does not mprove the performance of the classfer When a mnmum requred performance s reached. ISSN :

3 N. Svaram et. al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng Result Valdaton In real world applcatons, the selected features are evaluated by montorng the changes n the mnng algorthms performance. C. Decson Trees Decson tree s a tree structure, where nternal nodes denote a test on an attrbute, each branch represents the outcomes of the test and the leaf node represents the class labels. Decson tree nducton s the learnng of decson trees from class-labeled tranng tuples. Constructon of decson trees s smple and fast, and does not need any doman knowledge and hence approprate for exploratory knowledge dscovery. In general, decson tree classfers have good accuracy, but successful use of t depends on the data at hand. Decson trees are used for classfcaton and classfcaton rules are easly generated from them. An unknown tuple X can be classfed, gven ts attrbute values by testng the attrbute values aganst the decson tree. The three popular methods ID3, C4.5, and CART adopt a greedy, non-backtrackng approach n whch decson trees are constructed n a top-down recursve dvde-and-conquer manner [9],[13]. Analyss were made by constructng decson trees usng all the three algorthms and t was determned that C4.5 algorthm construct tree that classfes the recrutment data well. C4.5 algorthm, successor of ID3 uses gan rato as splttng crteron to partton the data set. ID3 s an teratve algorthm that uses nformaton gan as splttng crteron to construct the tree. For each attrbute A, the method calculates the nformaton gan as the dfference between the nformaton requred to classfy the data set based on just the proporton and the nformaton requred to classfy after parttonng on A. The expected nformaton needed to classfy a tuple n the tranng set D s gven by (1) [9],[12]: m Info( D) p log 2 ( p ) (1) 1 Where p s the probablty that an arbtrary tuple n D belongs to class C, and s estmated as the rato of number of nstances n class C n D to the total number of nstances n D. The amount of nformaton stll requred to classfy D, after splttng them usng A wth v possble values s calculated usng (2). v D j InfoA ( D) Info( D j ) D j1 Informaton gan obtaned by branchng the tranng set on the attrbute A s gven as n (3). Gan( A) Info( D) Info ( D) (3) A The C4.5 algorthm apples a knd of normalzaton to nformaton gan usng a splt nformaton value. Splt nformaton for an attrbute A wth v values s defned as n (4) [9]: (2) v D D Splt nf( A) log 2 (4) 1 D D Where D s the number of nstances n the tranng set D wth th value for the attrbute A and D s the total number of nstances n the tranng set. Gan rato s defned as n (5) and the attrbute wth maxmum gan rato s selected as the splttng attrbute [9]. Gan( A) Ganrato( A) (5) Splt nf( A) D. Heurstc Informaton Heurstc nformaton for each attrbute A n the feature space gves the approxmate mportance of A for the task of classfcaton. The maxmum of the condtonal probabltes of the attrbute A for ts possble values wth respect to the categores s used as the heurstc nformaton for the problem. The heurstc nformaton of an attrbute H A, when attrbutes A has V values wth respect to K categores s defned as n (6). H A v jk max ( 1 j1 p( A / C ) j E. Ant Colony Optmzaton Ant Colony Optmzaton s a metaheurstc algorthm that gudes and modfes other heurstcs to produce solutons beyond those that are normally generated n a quest for local optmalty [1]. Identfyng the optmal feature subset s a NPhard problem; n worst case exact algorthms need exponental tme to fnd the optmal soluton, hence approxmate algorthms also called as heurstc methods obtan good, whch are nearoptmal solutons at relatvely low computatonal cost. Ant Colony Optmzaton algorthm begns and proceeds randomly, at each pont of choce t randomly choose n ponts and analyze ther probablstc value. Node wth the hghest probablty value s chosen as next pont. The probablstc value for each pont n the search space s determned, based on ts heurstc nformaton and pheromone value [1], [5]. Ant colony optmzaton for feature selecton regard attrbutes of the tranng data as nodes n the feature space. Each of them possesses an ntal pheromone value and heurstc nformaton accordng to the mportance of the attrbute for classfcaton. The algorthm randomly starts wth a feature, and then chooses b, (b s the branchng factor at each pont) features randomly and determnes ther probablstc value usng (7) k P u u us 0 Where denotes the pheromone value and denotes heurstc nformaton for the feature, S s the set of features chosen randomly. and determnes the mportance of the (6) f S (7) Otherwse ISSN :

4 N. Svaram et. al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng pheromone value and heurstc nformaton respectvely. The feature wth the hghest probablstc value s ncluded n the constructed feature subset. The probablstc rule for the problem should balance the exploraton and explotaton of the search. Exploraton refers to the tendency of the algorthm to analyze the unvsted nodes and explotaton refers to the utlzaton of the search experence gathered from prevous teratons. Usng heurstc nformaton s mportant snce t gves the possblty of explotng, problem specfc knowledge [1]. Generally, two types of heurstc nformaton statc and dynamc are used. In statc method, heurstc nformaton s computed once at ntalzaton and remans unchanged throughout the whole algorthms run. Dynamc method, changes the heurstc nformaton accordng to the partal soluton constructed and computed at each step of the ant s walk [1]. Pheromone contents of all the paths decrease wth tme, to chance on the evaporaton rate and the pheromone contents of the paths taken by best performng ants are ncreased. The pheromone update rule s used as n (8) best ( t 1) (1 ) (8) Where (t) denotes the pheromone value at the th teraton, 0,1 denotes the evaporaton rate. The role evaporaton s to avod stagnaton that s the stuaton n whch all the ants constructs the same soluton. The quantty of pheromone that each ant has to depost s determned, by the qualty of the chosen features. The qualty of the selected features may be determned usng the performance of the classfer.. k k ( n S ) k ( S n 0 Otherwse f S k (t) (9) The equaton to determne the depost of pheromone s as n (9), Where ( S k s the performance of the classfer. and are the two parameters that control the relatve weght of the classfer performance and length of the feature subset. Pheromone update s done for the features selected by the teraton best ant global best ant, when hgh exploraton of search space s requred and the convergence tme s more for ths model. Pheromone update s done only for features selected by the global best ant whle fast convergence s requred. III. PROPOSED ACO FOR FEATURE SELECTION In the feature selecton algorthm n Table I, the attrbutes of the recrutment doman are treated as nodes; each node s assocated wth heurstc nformaton and an ntal pheromone value. Each ant starts wth an empty subset and start at a random pont. The proposed algorthm nvolves desgn of heurstc nformaton, update functon for pheromone value of the terms, and the lst of features whose pheromone value s updated. A. Update Pheromone Pheromone values of the selected terms are updated after estmatng the performance of the classfer wth the selected features. The features selected by the ants are taken as nput attrbutes and decson trees are constructed usng the tranng set, accuracy of the constructed decson trees s determned wth the testng set. The update functon deposts pheromone proportonal to the accuracy of the classfer. The proposed algorthm deposts pheromone on the features selected by both the global best and teraton best ant. TABLE I. PROPOSED FEATURE SELECTION ALGORITHM Input: D tranng set of sze N d ; T testng set of sze N - N d A set of Attrbutes n D C set of selecton categores n D of sze N c - ntal pheromone value 0 b branchng factor N ants number of ants N number of Iteratons n c number of features to be selected t threshold for feature ncluson r rato of features to be ncluded F set of features for each ant F best best subset of features Output: Selected Features Procedure ACO_for_Feature_Selecton 1: Intalze r and N. 2: For all a A do 3: Intalze a.pheromone = 0 4: For all v V, where V s the set of possble values of A 5: For all c j C, where C s the set of categores 6: a.heurstc_value 7: End For 8: End For 9: End For 10: n c A r 11: For ter = 1 to N j v k max ( 1 j1 p( A / C j )) ISSN :

5 N. Svaram et. al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng 12: Generate N ant ants 13: For ant 1 to ant Nant 14: For 1 to n c 15: L cp Lst of randomly generated b features 16: For all L cp L cp 17: p Apply probablstc rule 18: End For 19: ant j.f ant j.f Feature wth hghest p value n L cp 20: End For 21: Construct decson trees DT wth ant j.f for D 22: ACC j accuracy of DT for T 23: ACC ACC ACC j 24: End For 25: Fnd maxmum(acc) and the teraton best ant 26: Update Pheromone of Iteraton_Best_Ant.F and Global_Best_Ant.F 27: End For 28: Analyze the pheromone deposts and determne t 29: For 1 to N 30: Generate N ant ants 31: For ant 1 to ant Nant 32: L cp Lst of randomly generated b features 33: For all L cp L cp 34: p Apply probablstc rule 35: L thres L thres L cp f p of L cp > t 35: End For 36: ant j.f ant j.f max(l thres ) 37: Construct decson trees DT wth ant j.f for D 38: ACC j accuracy of DT for T 39: ACC ACC ACC j 40: End For 41: Fnd maxmum(acc) and the teraton best ant 42: Update Pheromone of nodes n Iteraton_Best_Ant.F and Global_Best_Ant.F 43: End For 44: F best Global_Best_Ant.F 32: Return F best IV. DATA SET AND EXPERIMENTAL RESULTS Ths secton descrbes the dataset used for expermentaton and the experments conducted and results. A. Data Set Data set requred for expermentaton was obtaned by knowledge acquston n an ndustry. The challenge nvolved n the process s that the knowledge s spread among the doman experts and s not wth a sngle person. Intervews were conducted wth the doman experts to understand the problem and the knowledge requred to solve the problem. The knowledge acqured s used along wth the recrutment database mantaned n the ndustry to form the dataset for expermentaton. The data collected from the ndustry s complex and have nosy, mssng and nconsstent data. The data s preprocessed to mprove the qualty of data and make t ft for the data mnng task. The data used are transformed nto approprate formats to support meanngful analyss. Some more attrbutes are derved usng the acqured knowledge to support the mnng process. The data set comprsed of recrutment detals of two years n an IT ndustry. Data set of one year was used as tranng set and the other was used for evaluatng the constructed model. The felds n the dataset nclude the canddates demographcal data and academc background such as frst name, last name, gender, natve state, student ID, year of brth, place of brth, name of hgh school, percentage of hgh school marks, name of hgher secondary school, percentage of hgher secondary marks, stream of dploma (If Applcable), percentage of dploma marks (If applcable), name of the college, stream of under graduaton, average of nternal marks n each semester, average of nternal marks n each semester, consoldate percentage n under graduaton, skll set n core ndustral competences rated by themselves n a scale of 1 to 10, marks secured n techncal test, marks secured n apttude test and marks secured n programmng test. Totally forty four attrbutes were outlned and approxmately three thousand records were collected. The status of selecton of the canddates, whch s the target varable ncludes dfferent values such as recommended, on hold and rejected. The recommended status ndcates that the canddate has been selected, on hold status ndcate that the canddate wll be selected f there s mmedate requrement and the status rejected ndcate that the canddate s not selected. Dataset1 conssts of 812 records and dataset2 conssts of 2192 records. The fnal status of the canddates after the recrutment process s tabulated n Table II. TABLE II. Fnal Status FINAL STATUS OF CANDIDATES IN THE DATASET Percentage of Records n Dataset1 Percentage of Records n Dataset ISSN :

6 N. Svaram et. al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng B. Expermental Results and Dscussons In the expermental setup of the ACO algorthm, the parameters,,, and must be assgned approprate values. In our experments, equal mportance was gven for the heurstc nformaton as well as the pheromone concentraton on nodes hence both and s set to 0.5. Evaporaton rate s set to 0.8 and =0.8 and =0.2. The algorthm was tested wth 500 ants by runnng upto 300 teratons. Decson trees were constructed by embeddng the data mnng tool weka, unpruned trees were constructed snce prunng conceals most of the vtal knowledge. Dataset 2 was used for tranng and dataset1 was used for testng. From Table 1, t may be observed, that the dataset conssts of more than 95% of records to be n the rejected category; hence the constructed trees were very excellent n recognzng the rejected data however they were not able to dentfy selected records to a large extent. Therefore the dataset was premedtated and decson trees were constructed wth almost equal number of records n both the categores. When the records were chosen for the learnng process, the dstrbuton of the status n the orgnal data was mantaned. The constructed decson trees were used to classfy all the records n the other dataset. Table III shows the performance of the proposed ACO algorthm. TABLE III. Number of Iteratons ASPECTS OF PERFORMANCE OF THE PROPOSED ACO ALGORITHM Number of attrbutes selected Detals of the attrbutes selected Marks obtaned n techncal, apttude and programmng tests, Stream of study n under graduaton, College name, natve cty, gender, percentage of marks secured n hgh school and hgher secondary school, nternal and external marks n all semester and total percentage n under graduaton Marks obtaned n techncal, apttude and programmng marks secured n hgh school and hgher secondary school, nternal and external marks n Accuracy of the classfer 67.12% 69.14% all semester and total percentage n under graduaton Marks obtaned n techncal, apttude and programmng marks secured n hgh school and hgher secondary school, stream of dploma and under graduaton, college name, gender, natve cty, natve state and total percentage n under graduaton Marks obtaned n techncal, apttude and programmng marks secured n hgh school and hgher secondary school, stream of under graduaton, college name and total percentage n under graduaton Marks obtaned n techncal, apttude and programmng marks secured n hgh school and hgher secondary school, stream of dploma and under graduaton, natve cty and total percentage n under graduaton Marks obtaned n techncal, apttude and programmng marks secured n hgh school and hgher secondary school, and total percentage n under graduaton 72.27% 75.31% 76.23% 79.77% ISSN :

7 N. Svaram et. al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng Accuracy of the constructed decson trees for the test dataset s as shown n Fg 1. The algorthm was stopped after 300 teratons snce there was no sgnfcant ncrease or decrease n performance of the classfer. Percentage_In_Hgher_Sec_School > 80 AND Average_Internal_Marks_In_BE>15 AND Average_External_Marks_In_BE>55 then Rejected Percentage of Accuracy The performance of the decson tree classfer wth all the attrbutes was around 61%, sgnfcant mprovement n the performance of the classfer was observed wth feature selecton as tabulated n Table 3. Analyses were made wth the decson trees wth good accuracy and 15 rules were deduced. The deduced rules were checked for vablty wth the doman experts and used n the recrutment process. table IV lsts few such rules. TABLE IV Number of Iteratons Fg 1 Accuracy of the Classfer at Dfferent Iteratons of the proposed algorthm RULES INFERRED FROM DECISION TREES WITH GOOD ACCURACY If Marks_In_Programmng > 25 AND Percentage_In_Hgher_Sec_School > 90 AND Marks_In_Techncal > 15 then Selected If College = X OR College = Y AND Mark_In_Apttude > 15 AND Percentage_In_BE > 70 AND Percentage_In_BE < 80 AND Percentage_In_Hgh_School > 85 AND Percentage_In_Hgher_Sec_School > 90 then Selected If College = not(x) OR College = not(y) AND Percentage_In_BE > 70 AND Percentage_In_BE < 80 AND Mark_In_Apttude < 10 then Rejected If College = X OR College = Y AND Percentage_In_Hgh_School > 90 AND Percentage_In_Hgher_Sec_School > 80 AND Percentage_In_Hgher_Sec_School < 90 AND Average_Internal_Marks_In_BE>10 AND Average_External_Marks_In_BE> 60 then Selected If College = not(x) AND College = not(y) AND Percentage_In_Hgh_School > 90 AND V. CONCLUSION The recrutment mnng problem was dentfed and the doman has been well studed by nteractng wth the doman experts. In the study, t was tact that the problem s NP-hard snce there s no drect optmal soluton whch would fnd the crtera for selecton. Also the set of nput attrbutes are not same for all the ndustres and usually vares every year. Decson trees were constructed wth hstorcal data collected from an ndustry and t was observed that the decson trees constructed wth subset of attrbutes was superor to the decson tree constructed wth the entre nput attrbute set. Hence an optmzaton algorthm based on ACO has been desgned and mplemented to dentfy the features that mpact the recrutment process. There was consderable ncrease n the accuracy of the classfer after few hundreds of teratons and rules were deduced from the decson trees wth good accuracy. The vablty of the deduced rules were verfed wth the doman experts and appled n the recrutment process. VI. REFERENCES [1] Marco Dorgo and Thomas Stutzle, Ant Colony Optmzaton, MIT Press, [2] Huan Lu and Le Yu, Towards Integratng Feature Selecton Algorthms for Classfcaton and Clusterng, IEEE Transactons on Knowledge and Data Engneerng, Vol., 17, No. 4, pp , Aprl [3] M. Dash and H. Lu, Feature Selecton for Classfcaton, Intellgent Data Analyss, pp , [4] George Formann, Feature Selecton we ve Barely scratched the surface, Hewlett Packard Laboratores, Palo Alto, [5] Ahmed Al-An, Ant Colony Optmzaton for Feature Subset Selecton, Proceedngs of World Academy of Scence, Engneerng and Technology, Vol. 4, pp , February [6] Thomas Stutzle and Holgar Hoos, Max-Mn Ant System And Local Search For The Travelng Salesman Problem, proceedngs of IEEE Internatonal Conference on Evolutonary Computaton, pp , [7] Chen-Fu Chen and L-Fe Chen, Data mnng to mprove personnel selecton and enhance human captal: A case study n hgh-technology ndustry, Expert Systems wth Applcaton, Elsever, Vol. 34: pp , [8] M. Sad Mehrabad and M. Fathan Brojeny, The development of an expert system for effectve selecton and appontment of the jobs applcants n human resource management, Computers and Industral Engneerng, Elsever, Vol. 53: , [9] Jawe Han, Mchelne Kamber, Data Mnng Concepts and Technques, Second Edton Morgan Kaufmann Publshers, San Francsco, [10] Flppo, E. B., Personnel management, McGraw-Hll Inc, U.S, [11] Scarpello, V. G., and Ledvnca, J. Personnel human resources management, South Western publshng company, U.S., [12] Lews, C.D. Employee selecton, Nelson Thrones Ltd., London, UK, [13] Usama M. Fayyad and Kek B. Iran Mult-nterval dscretzaton of contnuous valued attrbutes for classfcaton learnng, Thrteenth ISSN :

8 Internatonal Jont Conference on Artfcal Intellgence, France, pp , [14] Leo Breman, Jerome H. Fredman, Rchard A. Olshen, and Charles J. Stone, Classfcaton and Regresson Trees, Chapman and Hall/CRC, New York, N. Svaram et. al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng ISSN :