Hadoop based Feature Selection and Decision Making Models on Big Data

Transcription

1 Indan Jounal of Scence and Technology, Vol 9(0), DOI: /jst/206/v90/88905, Mach 206 ISSN (Pnt) : ISSN (Onlne) : Hadoop based Featue Selecton and Decson Makng Models on Bg Data Thulas Bkku, N. Sambasva Rao 2 and Ananda Rao Akepogu 3 Depatment of CSE, JNTUA, Anantapu 55002, Andha Padesh, Inda; thulas.bkku@gmal.com 2 SRITW, Waangal 50637, Telangana, Inda; snandam@gmal.com 3 Decto of Academcs and Plannng, JNTUCEA, Anantapu 55002, Andha Padesh, Inda; akepog@gmal.com Abstact Objectves: A lage amount of nfomatve data s beng captued and pocessed by today s oganzatons and s contnung to ncease exponentally. It becomes computatonally naccuate to analyze such bg data fo decson makng systems. Methods/Analyss: Hadoop, whch s a wokng model based on the Map-Reduce famewok wth effcent computaton and pocessng of Bg Data. Fndngs: Most of the tadtonal classfcaton algothms have ssues such as class mbalance and dmenson educton on Bg Data. Howeve, a lage pat of the data poduced today ae ncomplete and naccuate, so lage oganzatons pefe elatonal databases to stoe the nfomaton, but the use quey pocessng speed s vey low. Unlke exstng solutons that eque a po knowledge of classfcaton accuacy fo vaous types of data chaactestcs, whch s mpossble to obtan n pactce. Applcatons/Impovement: In ths pape, we have gven a compaed poposed model to dffeent bg data featue selecton and classfcaton models along wth advantages and lmtatons. Keywods: Bg Data, Decson Tee, Featue Selecton, Hadoop, MapReduce. Intoducton The tem Bg Data s useful to the computatonal data o nfomaton that can t be analyzed o handled usng a tadtonal machne leanng tools and technques. The geneal defnton of Bg Data ndcates computatonal data s too fast, massve o too had to pocess. Massve data can be geneated fom the clent seve applcatons whch ae known as sensos, etals, e-commece, fnancal sectos, medcal epostoes, etc. It also efes to the achtectues whch wee desgned o developed to stoe, captue, pocess and un volumes of data n lesse computatonal tme n eal tme. Hadoop s an open-souce cloud computng envonment of the Apache foundaton that povdes a dstbuted pogammng on lage datasets based on MapReduce. Its emakable featues nclude smplcty, fault-toleance and scalablty. Thee ae a numbe of MapReduce mplementatons such as Phoenx, Dyad, Sphee, Mas and Hadoop. Hadoop suppots the thee man challenges ceated by Bg data: Volume, Velocty and Vaety. In the tadtonal appoach, an oganzaton uses a seve to pocess the avalable data. But thee s an uppe bound to the amount of computatonal data because t s not capable and scalable as the data gows wth geat vaety and velocty. The MapReduce famewok suppots hgh dmensonal datasets, pattons them nto smalle sets and dstbutes them to dffeent cloud clustes fo computaton. MapReduce vew data as key, value pa as <Key, Value> as shown n Fgue. Hadoop uses fully abstact classes called Map and Reduce fom whch developes wll fomulate the poblems nto Map-Reduce fomat. Data ntensve pocessng s cuently consdeed nteest aound the MapReduce famewok fo lage scale data analytcs. In eal tme, t s a fault-toleant and scalable data pocessng tool, whch povdes the capablty to compute and pocess lage volumnous datasets n paallel wth many low-end computng cluste nodes. Howeve, MapReduce has nheent challenges on ts effcency and pefomance. Theefoe, many *Autho fo coespondence

2 Hadoop based Featue Selecton and Decson Makng Models on Bg Data Fgue. Hgh Dmensonal Data Data Patton P P2 P3 Map(P) Map(P2) Map(P3) Combne Solutons Geneal Wokflow of MapReduce Famewok. eseach woks have been endeavoued to ovecome the challenges of the MapReduce famewok. The man objectve of data mnng technques s to fnd knowledge fom lage datasets. The dscoveed knowledge suppots n decson makng systems. An ncease n ntensty and data set sze affects the computatonal effcency and also takes long tme to pocess. Howeve, most of the optmzaton models ae not desgned fo paallel computatonal envonments and the paallelzaton of the tadtonal algothms s dffcult to mplement and nontval. The decson tee s one of the key aeas n data mnng technologes. Cuent eseaches on decson tee algothm manly focus on optmzng the effcency, but not the data pocessng capablty. As the development of the netwokng and eal-tme applcatons nceases, the volumes of data also ncease exponentally. In ode to solve these ssues, a paallel dstbuted decson tee famewok usng Hadoop famewok s used to handle massve data. The emande of the pape s as follows. Secton 2 ntoduces the geneal ovevew of attbute selecton measues and data mnng appoaches usng the Hadoop famewok. Next, Secton 3, dscuss about the tadtonal expemental esults. Fnally, Secton 4 gves a concluson. 2. Paallel Rough-Set Featue Selecton In the pape, a paallel computaton of the equvalence classes and the attbute selecton ae mplemented fo attbute educton. They used paallel heachcal based attbute selecton algothm to fnd the decson ules on each level. The man lmtatons of ths attbute selecton method ae:. As the numbe of classes n sngle and mult-level heachcal pocess nceases then computatonal tme and space also nceases. 2. Ths model s based on entopy measue and unfom data dstbuton. Roughsets can be used to fnd the most elevant attbute selecton fom a gven data set wth dscetzed attbute values. Lowe and Uppe appoxmatons of decson class D c wth espect to a patton p Att ae defned as: Lowe bound appoxmaton Apox l (D ) = {x U/ Att c [x] Att D c }; Uppe bound Appoxmaton Apox u (D ) = {x U/ Att c [x] Att D c Ø}; Hee U s the nonempty fnte set (Unvese), Accuacy of Appoxmaton, Apox Acu whee R S = (U, R), S R and C U C denotes the cadnalty of C. Apox Acu S(C) = Apox l S(C) / Apox u S(C) If Apox Acu S(C) =, then C s csp wth espect to S. If Apox Acu S(C) <, then C s ough wth espect to S. An entopy measue used to mnmze the attbutes sze s gven as N Info( D) = ( n / n) log( n / n) = 0 Info( D/ Att) = ( n / n) ( n p = N = 0 / n ) log( n / n ) Whee n, n and n denotes the numbe of the objects, the numbe of objects equal to on D n Attbute, and the numbe of objects equal to I on D espectvely. 2. Pobablstc Based Measues Smlaly, Leung,Cason and Fan 2 poposed ankng nteval whch s dffeent fom ankng values explaned n pape. Attbute anked based on a elevance measue of uncetan data s used to estmate two nteval values. Let A = [ +,j ] and A2 = [ 2, + 2 ] be two nteval values. The degee of ankng measue fom A to A2 s defned as P(deg)[A >= A2] = mn{,max{( j )/(( j ) + ( j )), 0}} Smlaty of the two ntevals s gven as S=- P (deg) [A>=A2] - P (deg) [A2<=A] Assume that the two shot dscetzaton anges ae epesented as A= [2, 5] and A2= [3, 7], then 2 Vol 9 (0) Mach Indan Jounal of Scence and Technology

3 Thulas Bkku, N. Sambasva Rao and Ananda Rao Akepogu P (deg) [A>=A2] = mn {, max {(5-3) / ((5-2) + (7-3)), 0}} = mn {, max {0.28, 0}} = mn {, 0.28} = 0.28 P (deg) [A2<=A] = mn {, max {(7-2) / ((7-3) + (5-2)), 0}} = mn {, max {0.42, 0}} = mn {, 0.42} = 0.42 So, the degee of elevance between two objects s computed fo attbute selecton pocess s S = - P (deg) [A>=A2] - P (deg) [A2<=A] = = -0.4=0.86 Ths smlaty ndex ndcates the assocaton between two hghly elated attbutes fo constuctng a decson tee. An optmal featue subset extacted by a hgh dmensonal educton technque always depends on a cetan featue selecton measues. In geneal, dffeent measue may lead to dffeent optmzed attbute subsets. One of the majo ssues n the eal-tme dstbuted data s uncetanty and mssng values. Ths poblem ases; when moe than one attbute has the same data dstbuton that s dffeent attbutes get unfom data dstbuton. The man ssues n the tadtonal attbute based classfcaton models ae data cleanng, flteng and educton. Flteng analyss emoves all the edundant attbutes by attbute subset selecton. 2.2 Roughset based Random Foest Ou poposed system combnes the andom foest algothm wth oughset theoy to obtan bette esults. Roughset theoy s useful fo analysng the objects epesented by attbutes (featues). The basc assumptons n the oughest theoy ae: Objects ae epesented by attbute values and objects wth the same nfomaton ae ndscenble. Usng data mnng technques we ae able to constuct sngle tee, by usng Random Foest algothm poduces multple classfcaton tees. Random Foest s an effcent method, whch deals effectvely when a huge amount of data s mssng. In the below algothm we have used mapeduce, whch contans two functons map and educe. The map functon pocesses the data and geneates the key-value data nto decson tees. The educe functon educes the elevant and edundant data and gves classfcaton tees havng maxmum votes Poposed algothm fo Roughset based Random Foest Map Functon(Dataset) Input: Tanng nstances wth attbute set A Output: Decson Tee Rules Pocedue: In talze k paametes as k clustes n cloud envonment Intalze the dataset usng baggng algothm Buld tee pe bootstap by andomly selectng attbutes Whle attbute_set!= null do Fo each canddate attbutes do Co mpute the max of nfomaton gan(ig) as α α(a)=agmax IG; Splt the nfomaton attbute End End Reduce Functon Input: Set of Map Decson Tees Output: Classfy Result Pocedue: Poduces multple Decson Tees Check and compae the nodes n each decson tee Fnd the majoty votng tees fo the classfcaton Retun set of Decson Tee Rules 3. Data Pepocessng and Random Foest Model Data dscetzaton s a pocess of convetng contnuous data attbute values nto a fnte set of ntevals and has eceved moe and moe eseach attenton. The man eason s that tadtonal methods manly focus on leanng only nomnal attbutes, o only contnuous attbutes, but not mxed attbutes. Also, ule geneated though nducton ules o decson ules usng dscetzed data ae usually shote, compact and moe accuate than ules geneated by usng contnuous values. A dstbuted pattonng method fo data educton usng neaest neghbou classfcaton appoach s poposed 3. Ths model educed the numbe of nstances fom the ognal tanng data set. The man advantages of ths model ae senstve to nose and stoage ovehead. A ule dscovey Vol 9 (0) Mach Indan Jounal of Scence and Technology 3

4 Hadoop based Featue Selecton and Decson Makng Models on Bg Data model namely Rule MR usng the MapReduce model s explaned 4. Ths model s able to constuct a set of ules fom lage sets of nomnal valued attbutes. Ths model depends on use defned paametes that nfluence the tme computaton equements fo tanng data. In ths model, each clusteed node computes the nomalzed entopy and coveage facto as follows: ((Entopy(NC) Entopy(cond))/Entopy(NC)) * (P/E) Whee NC ndcates numbe of dffeent classes, cond s the class dstnct values, P s pobablty of exstence of each class and E s the estmaton paamete. Gan Rato whch s a successo of the nfomaton gan ovecomes the challenge of the nfomaton gan by usng a nomalzaton pocess a splt nfomaton value. But the man dawback wth the gan ato s that t geneates unbalanced splts to the unbalanced data. ELM-Tee decson tee 5 of whch the leaf nodes ae lnea egesson functons,..e. a new nstance s classfed by tavesng the tee fom the oot to a leaf and detemned the pedcton value wth the lnea egesson functon n the leaf node as shown n Fgue 2. The poposed ELM tee 5 doesn t applcable to ncemental tee constucton on lage datasets. Also, ths appoach doesn t handle mxed type of attbutes usng MapReduce famewok. Saa del Ro, Vctoa and Manuel poposed a model to ovecome the class mbalance poblem, whee each class has dffeent data dstbuton and attbutes type. Ovesamplng and undesamplng ssue n lage datasets s handled usng Random foest algothm fo classfcaton 6. The dawbacks 5 ae pefomance depends on the numbe of ntemedate mappes used n MapReduce Famewok. It needs to detemne the statc theshold fo the mnoty class wth espect to the numbe of ntemedate mappes to get bette pefomance. It needs to mpove the statc classfcaton and statc paametes when a MapReduce famewok s used. The mbalanced dataset ssue n classfcaton models may occu when the numbe of tuples o nstances that epesent one class s much moe than the othe classes. Thee ae many felds n whch mbalance occus between the dffeent classes such as sk management, satellte mages, medcal data, senso data, and tme sees data, etc. Although the hype netwok gaph theoy has been used n solvng vaous classfcaton ssues, t usually esults n poo pefomance when dealng wth class mbalance ssues. Lke most of the conventonal methods, hype netwok models assume that the class and data dstbuton of data sets ae balanced 7. Both the sngle labelled classfcaton and Mult labelled classfcaton ae mpotant eseach aeas n supevsed leanng. Howeve, nethe of them can ovecome the mbalance ssue whch has a negatve effect on the classfe accuacy. Addessng mbalance ssues n mult labelled classfcaton s moe complex and t s even dffcult to defne the label dstbuton. Isaac, Danel pealta and salva use M = R d, denotes the d-dmensonal vecto space and N = {n, n 2, n q }, n {0, } denotes the bnay label vecto space 8. The mult labeled data set can be epesented as: D= {(p, q ) <= <= c,p R,q N} Whee d, q, c epesents the numbe of attbutes, numbe of class labels and numbe of nstances. MRPR mplemented a decson tee famewok to geneate decson ules at each level n multdmensonal model 8. The man am of ths model s to optmze the decson ules wth mnmal ule-set. Mnng ules on each level s based on multdmensonal data model as shown n Fgue 3. Ths famewok has two phases, one s data Reducton phase and the othe s decson ule geneaton phases 9. In the fst phase, attbute educton DB d Dstbuted Data Souces DB2.. Data Integaton & Roughset Model DB-n Fgue 2. Fgue Tadtonal 2. C4.5 and ELM Tee Data Classfcaton. Fgue 3. Fgue 3. Rough-set model wth Random Foest Usng MapReduce. 4 Vol 9 (0) Mach Indan Jounal of Scence and Technology

5 Thulas Bkku, N. Sambasva Rao and Ananda Rao Akepogu and attbute flteng pocedues ae pefomed on the nput data set.and n the second phase, decson ules usng heachcal ough-set and ntepet the elatonshp between decson ules mned fom dffeent levels of heachy. The poblems obseved ae t needs to mpove attbute educton at multple levels, wth dynamc theshold paametes to appoxmate decson ules 8. Table shows the tadtonal classfcaton algothms, whch cannot handle huge amounts of data. Table descbes the dffeent classfcaton models and ts capable featues such as attbute selecton measues, scalablty, paallel suppot, decson tee stuctue and hadoop famewok suppot. In ou eseach wok, we have mplemented these models n hadoop famewok fo pefomance analyss whch gves bette esults. 4. Expemental Results of Tadtonal Appoaches In ths secton, we have evaluated the pefomance of the oughset attbute selecton measue wth dffeent Hadoop based classfcaton models. All the expements ae caed-out on the Hadoop famewok, whch s an open souce that suppots dstbuted applcatons. Hadoop famewok and Netbeans IDE veson 8 ae used to execute the MapReduce famewok. Fo data stoage, Amazon AWS cloud seve wth lage nstances s used to execute multple lage data sets on the dffeent cluste nodes. Lage data sets ae downloaded fom the UCI epostoy, whch conssts of 4 attbutes wth 0 decson classes and lage numbe of nstances. Table 2 descbes the nfomaton about the total numbe of nstances used n ou expement and ts statstcal Table. Decson Tee algothms fo Bg data suppotng paametes Algothm ID3 Selecton Technque Infomaton Gan Scalablty Paallelsm Tee Stuctue Bgdata Handle Poo Poo Mult-tee No C4.5 Gan Rato Poo Poo Mult-tee No CART SLIQ SPRINT PUBLIC Gn Coeffcent Gn Coeffcent Gn Coeffcent Gn Coeffcent Poo Poo Bnay No Good Good Bnay No Good Good Bnay No Poo Poo Bnay No nfomaton. Poposed hadoop based featue selecton model was tested on dffeent classfcaton models to test the classfcaton accuacy fo lage datasets. Kddcup 99 ntuson dataset was used on dffeent classfcaton models such as Genetc Algothm- Featue Selecton Algothm, Neual Netwoks, ELM tee and Random Foest. By usng ough-set featue educton model, elevant attbutes fo attack detecton ae dentfed. Table 3 and Fgue 4 descbe the pefomance analyss of mappe and educe hadoop nteface classes usng featue educton pocess. Random Foest tee and oughset ae ntegated n hadoop envonment to fnd the tme complexty of the Mappe and Reduce ntefaces. As shown n the table, we have obseved that tme complexty educton n the Mappe and Reduce phases of Random foest wth Roughset model compaed to othe classfcaton models wthout oughset. Table 4 and Fgue 5 descbe the pefomance analyss of classfcaton models. Random Foest tee wth Table 2. Hadoop based Rough-set based featues selecton model Dataset Sze Total-Featues Roughset based Featues Reduced Table 3. Hadoop based Classfcaton model usng Rough-set featue selecton Algothm MappeTme (mns) ReduceTme (mns) GA_FSA Neual Netwok ELM-Tee Random Foest (Roughset) Attack Classes Kdd Kdd Kdd Kdd Fgue 4. Fgue 4. Algothms wth MapReduce Statstcs. Vol 9 (0) Mach Indan Jounal of Scence and Technology 5

6 Hadoop based Featue Selecton and Decson Makng Models on Bg Data Table 4. Tadtonal classfcaton Algothms wth Tue Postve and Eo Rates Algothm Tue Postve (%) Eo (%) Outle (%) GA_FSA NeualNetwok ELM-Tee Random Foest (Roughset) n pactce. Tadtonal models need to mpove executon pocess and classfcaton accuacy of the complex dstbuted databases. The man lmtatons of these models ove bg data ae outle ssue, scalng up fo hgh dmensonal data classfcaton ssue, mnng spase data ssue and constaned optmzaton ssue. Hence, one of ou futue woks s to pesent a novel attbute selecton based paallel classfe to pocess mxed attbutes on lage datasets. 6. Refeences Fgue 5. Rates. Pefomance analyss of Tue Postve and Eo ough-set gets hgh tue postve classfcaton ate fo ntuson detecton compaed to othe classfcaton models n hadoop famewok. Tue postve ate ndcates the numbe of postve nstances eflects the attacks compae to negatve samples. Eo (%) ndcates the numbe of msclassfed nstances. Also, the outles (%) whch ndcates the numbe of nstance that s not elevant to the exstng attacks behavou ae also computed. 5. Concluson In ths pape, we have studed the Rough-set based featue selecton model on the andom foest n Hadoop famewok. Poposed model s compaed wth the tadtonal classfcaton models along wth lmtatons and pefomance measues. Expemental esults show that poposed model pefoms well aganst the tadtonal models n Hadoop famewok. Unlke exstng solutons that eque a po knowledge of classfcaton accuacy fo vaous types of data chaactestcs, whch s mpossble to obtan. Qan J, Lv P, Yue X, Lu C, Jng Z. Heachcal attbute educton algothms fo bg data usng MapReduce. Knowledge-Based Systems. 205; 73: Leung CK, Jang F. A data scence soluton fo mnng nteestng pattens fom uncetan bg data. In 204 IEEE 4th Intenatonal Confeence on Bg Data and Cloud Computng. Sydney, NSW p Fayed HA, Atya AF. A Novel template educton appoach fo the-neaest neghbo method. IEEE Tansactons on Neual Netwoks. 2009; 20(5): Kolas V, Kolas C, Anagnostopoulos I, Kayafas E. Rule MR: Classfcaton ule dscovey wth MapReduce. In 204 IEEE Intenatonal Confeence on Bg Data (Bg Data). Washngton: DC p Wang R, He YL, Chow CY, Ou FF, Zhang J. Leanng ELMtee fom bg data based on uncetanty educton. Fuzzy Sets and Systems. 205; 258: Del Río S, López V, Benítez JM, Heea F. On the use of MapReduce fo mbalanced bg data usng andom foest. Infomaton Scences. 204 Nov; 285: Feng Q, Mao D, Cheng Y. Heachcal decson ules mnng. Expet systems wth applcatons. 200 Ma; 37(3): Tgueo I, Pealta D, Bacadt J, Gacía S, Heea F. MRPR: a MapReduce soluton fo pototype educton n bg data classfcaton. Neuocomputng. 205 Feb; 50: Noh KS, Lee DS. Bgdata Platfom Desgn and Implementaton Model. Indan Jounal of Scence and Technology. 205; 8(8): 8. 6 Vol 9 (0) Mach Indan Jounal of Scence and Technology