The Impact of Feature Selection on Web Spam Detection

Transcription

1 I.J. Itelliget Systems ad Applicatios, 2012, 9, Published Olie August 2012 i MECS ( DOI: /ijisa The Impact of Feature Selectio o Web Spam Detectio Jaber Karimpour Dept. of Computer Sciece, Uiversity of Tabriz, Tabriz, Ira karimpour@tabrizu.ac.ir Ali A. Noroozi Dept. of Computer Sciece, Uiversity of Tabriz, Tabriz, Ira aliasghar.oroozi@gmail.com Adeleh Abadi Dept. of Computer Sciece, Uiversity of Tabriz, Tabriz, Ira adeleh.abadi@gmail.com Abstract Search egie is oe of the most importat tools for maagig the massive amout of distributed web cotet. Web spammig tries to deceive search egies to rak some pages higher tha they deserve. May methods have bee proposed to combat web spammig ad to detect spam pages. Oe basic oe is usig classificatio, i.e., learig a classificatio model for classifyig web pages to spam or o-spam. This work tries to select the best feature set for classificatio of web spam usig imperialist competitive algorithm ad geetic algorithm. Imperialist competitive algorithm is a ovel optimizatio algorithm that is ispired by socio-political process of imperialism i the real world. Experimets are carried out o WEBSPAM- UK2007 data set, which show feature selectio improves classificatio accuracy, ad imperialist competitive algorithm outperforms GA. Idex Terms Web Spam Detectio, Feature Selectio, Imperialistic Competitive Algorithm, Geetic Algorithm I. Itroductio With the explosive growth of iformatio o the web, it has become the most successful ad giat distributed computig applicatio today. Billios of web pages are shared by millios of orgaizatios, uiversities, researchers, etc. Web search provides great fuctioality for distributig, sharig, orgaizig, ad retrievig the growig amout of iformatio [1]. Search egies have become more ad more importat ad are used by millios of people to fid ecessary iformatio. It has become very importat for a web page, to be raked high i the importat search egies results. As a result may techiques are proposed to ifluece rakig ad improve the rak of a page. Some of these techiques are legal ad are called Search Egie Optimizatio (SEO) techiques, but some are ot legal or ethical ad try to deceive rakig algorithms. These spam pages try to rak pages higher tha they deserve [2]. Web spam refers to web cotet that get high rak i search egie results despite low iformatio value. Spammig ot oly misleads users, but also imposes time ad space cost to search egie crawlers ad idexers. That is why crawlers try to detect web spam pages to avoid processig ad idexig them. Cotet-based spammig methods basically tailor the cotets of the text fields i HTML pages to make spam pages more relevat to some queries. This kid of spammig is also called term spammig. There are two mai cotet spammig techiques, which simply create sythetic cotets cotaiig spam terms: repeatig some importat terms ad dumpig may urelated terms [3,4]. Lik spammig misuses lik structure of the web to spam pages. There are two mai kids of lik spammig. Out-lik spammig tries to boost the hub score of a page by addig out-liks i it poitig to some authoritative pages. Oe of the commo techiques of this kid of spammig is directory cloig, i.e., replicatig a large portio of a directory like Yahoo! i the spam page. I-lik spammig refers to persuadig other pages, especially authoritative oes, to poit to the spam page. I order to do this, a spammer might adopt these strategies: creatig a hoey pot, ifiltratig a web directory, postig liks o usergeerated cotet, participatig i lik exchage, buyig expired domais, ad creatig ow spam farm [2]. Hidig techiques are also used by spammers who wat to coceal or to hide the spammig seteces, terms, ad liks so that web users do ot see those [3]. Cotet hidig is used to make spam items ivisible. Oe simple method is to make the spam terms the same color as the page backgroud color. I cloakig, spam Copyright 2012 MECS I.J. Itelliget Systems ad Applicatios, 2012, 9, 61-67

2 62 The Impact of Feature Selectio o Web Spam Detectio web servers retur a HTML documet to the user ad a differet documet to a web crawler. I redirectig, a spammer ca hide the spammed page by automatically redirectig the browser to aother URL as soo as the page is loaded. I two latter techiques, the spammer ca preset the user with the iteded cotet ad the search egie with spam cotet [5]. Various methods have bee proposed to combat web spammig ad to detect spam pages. Oe importat ad basic type of methods is cosiderig web spam detectio as a biary classificatio problem [4]. I this kid of methods, some web pages are collected as traiig data ad labeled as spam or o-spam by a expert. The, a classifier model is leared from the traiig data. Oe ca use ay supervised learig algorithm to build this model. Further, the model is used to classify ay web page to spam or o-spam. The key issue is to desig features used i learig. Ntoulas et al. [4] propose some cotet-based features to detect cotet spam. Lik-based features are proposed for lik spam detectio [6,7]. Liu et al. [8] propose some user behavior features extracted from access logs of web server of a page. These features depict user behavior patters whe reachig a page (spam or o-spam). These patters are used to separate spam pages from o-spam oes, regardless of spammig techiques used. Erdelyi et al. [9] ivestigate the tradeoff betwee feature geeratio ad spam classificatio accuracy. They coclude that more features achieve better performace; however, the appropriate choice of the machie learig techiques for classificatio is probably more importat tha devisig ew complex features. Feature selectio is the process of fidig a optimal subset of features that cotribute sigificatly to the classificatio. Selectig a small subset of features ca decrease the cost ad the ruig time of a classificatio system. It may also icrease the classificatio accuracy because irrelevat or redudat features are removed [10]. Amog the may methods proposed for feature selectio, evolutioary optimizatio algorithms such as geetic algorithm (GA) have gaied a lot of attetio. Geetic algorithm has bee used as a efficiet feature selectio method i may applicatios [11,16]. I this paper, we icorporate geetic algorithm, ad imperialist competitive algorithm [12] to fid a optimal subset of features of the WEBSPAM-UK2007 data set [13,14]. The selected features are used for classificatio of the WEBSPAM-UK2007 data. The rest of the paper is orgaized as follows. Sectio 2 gives a brief itroductio of the imperialist competitive algorithm (ICA). Sectio 3 describes the feature selectio process by ICA ad GA. Experimetal results are discussed i sectio 4, ad fially, sectio 5 cocludes the paper. II. Imperialistic Competitive Algorithm The imperialist competitive algorithm is ispired by imperialism i the real world [12]. Imperialism is the policy of extedig the power of a coutry beyod its boudaries ad weakeig other coutries to take cotrol of them. Fig. 1 Iitializatio of the empires: The more coloies a imperialist possesses, the bigger is its mark [12] This algorithm starts with a iitial society of radom geerated coutries. Some of the best coutries are selected to be imperialists ad others are selected to be coloies of these imperialists. The power of a empire which is the couterpart of fitess value i geetic algorithms, is the power of the imperialist coutry plus a percetage of mea power of its coloies. Figure 1 depicts the iitializatio of the empires. After assigig all coutries to imperialists, ad formig empires, coloies start movig towards the relevat imperialist (Assimilatio). The, some coutries radomly chage positio i the search space (Revolutio). After assimilatio ad revolutio, a coloy may get a better positio i the search space ad take cotrol the empire (substitutio for the imperialist). Fig. 2 Imperialistic competitio: The weakest coloy of the weakest empire is possessed by other empires [12] The, imperialistic competitio begis. All empires try to take cotrol of the weakest coloy of the weakest empire. This competitio reduces the power of weaker empires ad icreases the power of the powerful oes. Ay empire that caot compete with other empires ad icrease its power or at least prevet decreasig it, will Copyright 2012 MECS I.J. Itelliget Systems ad Applicatios, 2012, 9, 61-67

3 The Impact of Feature Selectio o Web Spam Detectio 63 gradually collapse. As a result, after some iteratios, the algorithm coverges ad oly oe imperialist remais ad all other coutries are coloies of it. Figure 2 depicts the imperialistic competitio. The more powerful a empire is, the more likely it will take cotrol of the weakest coloy of the weakest empire. The pseudo code of ICA is as follows: 1. Iitialize the empires 2. Assimilatio: Move the coloies toward their relevat imperialist 3. Revolutio: Radomly chage the characteristics of some coloies 4. Exchage the positio of a coloy ad Imperialist. If a coloy has more power tha that of imperialist, exchage the positios of that coloy ad the imperialist 5. Compute the total power of all empires 6. Imperialistic competitio: Give the weakest coloy from the weakest empire to the empire that has the most likelihood to possess it 7. Elimiate the powerless empires 8. If there is just oe empire, stop, else, go to 2 III. Feature Selectio WEBSPAM-UK2007 data set cotais 96 cotet based features. We use the imperialist competitive ad geetic algorithms to optimize the features that cotribute sigificatly to the classificatio. A. Feature Selectio Usig ICA I this sectio, the steps of feature selectio usig ICA are described. 1) Iitialize the empires I the geetic algorithm, each solutio to a optimizatio problem is a array, called chromosome. I ICA, this array is called coutry. I feature selectio, each coutry is a array of biary umbers. Whe coutry[i] is 1, the i th feature is selected for classificatio, ad whe it is 0, the i th feature is removed [15]. Figure 3 depicts the feature represetatio as a coutry. F F F Coutry F -1 F Feature subset = {F, F,..., F } Fig. 3 Feature represetatio as a coutry i ICA [15] The power of each coutry is calculated by F-score. F-score is a commoly used measure i machie learig ad iformatio retrieval [3,10]. The cofusio matrix of a give a classifier is cosidered as table 1. Table 1. Cofusio Matrix Classified spam Classified o-spam Actual spam A B Actual o-spam C D F-score is determied as follows F-score = 1 / (1 / Recall + 1 / Precisio) (1) Where Recall, ad Precisio are defied as follows Recall = C / (C + D) (2) Precisio = B / (B + D) (3) The algorithm starts by radomly iitializig a populatio of size N. N of the most powerful pop imp coutries are selected as imperialists ad form the empires. The remaiig coutries ( ) are assiged to empires based o the power of each empire. The ormalized power of each imperialist is defied by NP P N imp i 1 P i N Where P is the power of coutry. The iitial umber of coloies of col col empire will be (4) NC roud { NP * N } (5) To assig coloies to empires, is chose radomly ad assiged to coloies alog with the NC of the coloies imperialist. These imperialist will form empire. 2) Assimilatio I this phase, coloies move towards the relevat imperialist. Sice feature selectio is a discrete problem, we use followig operator for assimilatio [15] For each coloy Create a biary strig ad assig a radom geerated biary to each cell Copy the cells of the relevat imperialist, correspodig to the locatio of 1 s i the biary strig, to the same positios i the coloy 3) Revolutio The purpose of revolutio is preservig ad itroducig diversity. It allows the algorithm to avoid local miimum. Revolutio occurs accordig to a user defied revolutio probability. For each coloy, some cells are selected radomly ad their cotaiig biary is iverted ( 1 is iverted to 0, ad 0 is iverted to 1 ). Copyright 2012 MECS I.J. Itelliget Systems ad Applicatios, 2012, 9, 61-67

4 64 The Impact of Feature Selectio o Web Spam Detectio 4) Exchage the positios of a coloy ad imperialist After assimilatio ad revolutio, a coloy may gai more power tha that of imperialist. As a result, the best coloy of a empire ad its imperialist exchage positios. The, the algorithm will cotiue by the imperialist i a ew positio. 5) Compute the total power of empires The total power of a empire is maily affected by the power of its imperialist. Aother factor i computig the total power of a empire is the power of coloies of that empire. Of course, the mai power is by the power of the imperialist, ad the power of coloies has less impact. As a result, we defie the total power of empire is defied TP power ( imperialist ) mea{ power ( coloies of empire )} (6) Where is a positive factor which is cosidered to be less tha 1. Decreasig the value of icreases the role of the imperialist i determiig the total power of a empire ad icreasig it will icrease the role of the coloies. 6) Imperialistic Competitio I this importat phase of the algorithm, the empires compete to take cotrol of the weakest coloy of the weakest empire. Each empire has a likelihood of possessig the metioed coloy. The possessio probability of empire is obtaied by P emp TP i 1 N imp TP i (7) As you ca otice, the most powerful empire does ot take possessio of the weakest coloy of the weakest empire, but it will be more likely to possess the metioed coloy. 7) Elimiate the powerless empires Imperialistic competitio causes some empires to lose power ad gradually collapse. Whe a empire loses all its coloies, we assume it is collapsed ad elimiate it. The imperialist of this powerless empire is possessed by other empires as a coloy. 8) Covergece As a result of imperialistic competitio ad elimiatio of powerless empires, the algorithm will coverge to the most powerful empire ad all the coutries will be uder the cotrol of this empire. The imperialist of this empire will determie the optimal subset of features selected for classificatio, because this imperialist is the most powerful of all coutries. B. Feature selectio usig GA I the geetic algorithm, each solutio to the feature selectio problem is a strig of biary umbers, called chromosome. Whe chromosome[i] is 1, the i th feature is selected for classificatio, ad whe it is 0, the i th feature is ot selected [11,16]. The fitess fuctio is cosidered the accuracy of the classificatio model. I this research, we calculate the fitess value of each chromosome by F-score. F-score was described i the previous sectio. The algorithm starts by radomly iitializig a populatio of size N. The, crossover ad mutatio are doe. pop Crossover allows the geeratio of ew chromosomes by combiig curret best chromosomes. To do crossover, sigle poit crossover techique is used, i.e., oe crossover poit is selected, biary strig from begiig of chromosome to the crossover poit is copied from oe paret, the rest is copied from the secod paret. Figure 4 shows how childre are geerated from each pair of chromosomes by crossover. Mutatio is similar to revolutio i ICA. It maitais geetic diversity ad allows the algorithm to avoid local miimum. To do mutatio, i each chromosome, a radom cell is selected ad its cotaiig bit i iverted ( 1 is iverted to 0, ad 0 is iverted to 1 ). Mutatio ad crossover occur accordig to a previously defied mutatio ad crossover probability. Geetic algorithm iterates for some user defied umber of geeratios. Fig. 4 how childre are geerated from parets by crossover [17] IV. Experimetal Results I order to ivestigate the impact of feature selectio o web spam classificatio, WEBSPAM-UK2007 data are used. It is a publicly available web spam data collectio ad is based o a crawl of the.uk domai doe i May 2007 [13, 14]. It icludes 105 millio pages ad over 3 billio liks i hosts. The traiig set cotais 3849 hosts. This data set cotais cotet ad lik based features. I our experimets, we used oly cotet based features because they were eough to meet our purposes. The selected data set cotais 3849 data, with 208 spam ad 3641 o-spam pages. We partitioed this data set to two disjoit sets: traiig data set with 2449 data, ad test data set with 1000 data. After performig feature Copyright 2012 MECS I.J. Itelliget Systems ad Applicatios, 2012, 9, 61-67

5 The Impact of Feature Selectio o Web Spam Detectio 65 selectio usig the traiig set, the test set was used to evaluate the selected subset of features. The evaluatio of the overall process was based o weighted f-score which is a suitable measure for the spam classificatio problem. It was also used as the power fuctio i ICA ad fitess fuctio i GA. Bayesia Network, Decisio Tree (C4.5 algorithm), ad Support Vector Machie (SVM) were chose as learig algorithms to perform the classificatio ad calculate the weighted F-score. These algorithms are powerful learig algorithms used i may web spam detectio researches [4, 5, 18]. Followig parameters were used for ICA Number of coutries = 100 Number of imperialists = 10 = 0.1 Revolutio rate = 0.01 Selected parameters for GA are as follows Iitial populatio = 100 Number of geeratios (iteratios) = 100 Crossover rate = 0.6 Mutatio rate = 0.01 Figure 5 depicts maximum ad mea power of all imperialists versus iteratio, usig Decisio Tress, SVM, ad Bayesia Network classifiers, i ICA. As show i this figure, by SVM ad Decisio Tree classifiers, the global maximum of the fuctio (maximum power) is foud i less tha 5 iteratios, while by Bayesia Network, it is foud i 12 th iteratio. Fig. 5 Mea ad maximum power of all imperialists versus iteratio, usig differet classifiers, i ICA Figures 6, ad 7 compare ICA power fuctio ad GA fitess fuctio versus iteratio. Figure 6 shows the power (fitess) of best aswer versus iteratio (geeratio), usig Bayesia Network classifier, i ICA ad GA. As you ca see, ICA coverges faster tha GA, ad has more power tha GA i all iteratios. Aother importat poit is that the iitial value of f-score which is the result of radom iitializatio of populatio i both algorithms, gets a higher icrease by ICA over iteratios. This poit shows that imperialistic competitio outperforms geetic evolutio i the problem of spam classificatio. Fig. 6 Power (fitess) of best aswer versus iteratio, usig Bayesia Network classifier Copyright 2012 MECS I.J. Itelliget Systems ad Applicatios, 2012, 9, 61-67

6 66 The Impact of Feature Selectio o Web Spam Detectio Fig. 7 Mea power (fitess) of all aswers versus iteratio, usig Bayesia Network classifier Figure 7 depicts mea power (fitess) of all aswers versus iteratio, usig Bayesia Network classifier, i ICA ad GA. As you ca see, ICA gets a higher icrease i mea power of all aswers. The optimal subset of features selected by ICA ad GA are used to trai a classificatio model. This model is evaluated by the test data set. Evaluatio results obtaied for Bayesia Network, Decisio Tree, ad SVM classifiers are show i table 2. These results idicate that feature selectio by both ICA ad GA techiques improves web spam classificatio. Furthermore, ICA based feature selectio outperforms GA based feature selectio i the problem of web spam detectio. Table 2 The impact of ICA ad GA based feature selectio o web spam classificatio, usig differet classifiers Bayesia Network Decisio Tress SVM Number of features F-score Number of features F-score Number of features F-score All features GA ICA V. Coclusio I this paper, we studied the impact of feature selectio o the problem of web spam classificatio. Feature selectio was performed by Imperialist Competitive Algorithm ad Geetic Algorithm. Experimetal results showed that selectig a optimal subset of features icreases classificatio accuracy, but ICA could fid better optimal aswers tha GA. I fact, we observed that reducig the umber of features decreases the classificatio cost ad icreases the classificatio accuracy. Other optimizatio methods, such as PSO ad at coloy ca be used for feature selectio ad compared with ICA ad GA i future works. Refereces [1] Caverlee J, Liu L, Webb S. A Parameterized Approach to Spam-Resiliet Lik Aalysis of the Web. IEEE Trasactios o Parallel ad Distributed Systems (TPDS), 2009, 20: [2] Gyogyi Z,Garcia-Molia H. Web spam taxoomy. I: First iteratioalworkshop o adversarial iformatio retrieval o the web (AIRWeb 05), Japa, [3] Liu B. Web Data Miig, Explorig Hyperliks, Cotets, ad Usage Data. Spriger, [4] Ntoulas A, Najork M, Maasse M, et al. Detectig Spam Web Pages through Cotet Aalysis. I Proc. of the 15th Itl. World Wide Web Coferece (WWW 06), [5] Wag W, Zeg G, Tag D. Usig evidece based cotet trust model for spam detectio. Expert Systems with Applicatios, (8): [6] Becchetti L, Castillo C, Doato D, et al. Lik-based characterizatio ad detectio of Web Spam. I Proc. Of 2d It. Workshop o Adversarial Copyright 2012 MECS I.J. Itelliget Systems ad Applicatios, 2012, 9, 61-67

7 The Impact of Feature Selectio o Web Spam Detectio 67 Iformatio Retrieval o the Web (AIRWeb 06), Seattle, WA, [7] Castillo C, Doato D, Giois A, et al. Kow your eighbors: Web spam detectio usig the web topology. I Proc. Of 30th Au. It. ACM SIGIR Cof. Research ad Developmet i Iformatio Retrieval (SIGIR 07), New York, [8] Liu Y, Ce R, Zhag M, et al. Idetifyig web spam with user behavior aalysis. I Proc. Of 4th It. Workshop o Adversarial Iformatio Retrieval o the Web (AIRWeb 08), Chia, [9] Erdelyi M, Garzo A, Beczur A A. Web spam classificatio: a few features worth more. I Proceedigs of the 2011 Joit WICOW/AIRWeb Workshop o Web Quality2011, Idia, [10] Ha J, Kaber M, Pei J. Data Miig, Cocepts ad Techiques. 3 rd ed, Morga Kaufma, [11] Vafaie H, De Jog K. Geetic algorithms as a tool for feature selectio i machie learig. I Proceedigs of Fourth Iteratioal Coferece o Tools with Artificial Itelligece (TAI '92), [12] Atashpaz-Gargari E, Lucas C. Imperialist competitive algorithm: A algorithm for optimizatio ispired by imperialistic competitio. IEEE Cogress o Evolutioary Computatio (CEC 2007), [13] Castillo C, Doato D, Becchetti L, et al. A referece collectio for webspam. SIGIR Forum, 2006, 40(2): [14] Yahoo Research. Web Spam Collectios. [cited 2011 May], Available from: ets/, 2007 [15] Mousavi Rad S J, Mollazade K, Akhlagia Tab F. Applicatio of Imperialist Competitive Algorithm for Feature Selectio: A Case Study o Bulk Rice Classificatio. Iteratioal Joural of Computer Applicatios, (16):41-48 [16] Yag J, Hoavar V. Feature subset selectio usig a geetic algorithm. Itelliget Systems ad their Applicatios, IEEE, (2): [17] Eibe A E, Smith J E. Itroductio to Evolutioary Computig, Spriger, [18] Araujo L, Martiez-Romo J. Web Spam Detectio: New Classificatio Features Based o Qualified Lik Aalysis ad Laguage Models. IEEE Trasactios o Iformatio Foresics ad Security, (3): KARIMPOUR Jaber ( ), male, Tabriz, Ira, Assistat Professor, his research directios iclude verificatio ad formal methods. NOROOZI Ali A. (1986-), male, Tabriz, Ira, Master of Sciece, his research directios iclude adversarial iformatio retrieval ad distributed systems. ABADI Adeleh ( ) female, Tabriz, Ira, Master of Sciece, his research directios iclude verificatio ad formal methods. Copyright 2012 MECS I.J. Itelliget Systems ad Applicatios, 2012, 9, 61-67