Optimizing Naïve Bayes Algorithm for SMS Spam Filtering on Mobile Phone to Reduce the Consumption of Resources

Transcription

1 Journal of Computers Vol. 28, No. 3, 2017, pp do: / Optmzng Naïve Bayes Algorthm for SMS Spam Flterng on Moble Phone to Reduce the Consumpton of Resources L-qun Bao 1*, Ln-xa LV 2, and Jn-Long L 1 1 College of Electroncs and Informaton Engneerng, Lanzhou Insttute of Technology Lanzhou, Chna baolqun1983@163.com 2 College of Software Engneerng, Lanzhou nsttute of technology Lanzhou, Chna Receved 25 May 2016; Revsed 09 December 2016; Accepted 09 December 2016 Abstract. Moble phone s a embedded system wth lmted energy, memory and computng ablty. The tradtonal SMS spam flterng algorthms only focused on the accuracy rate of spam flters and dd not consder the consumpton of moble phone s hardware resources. Ths paper proposes an optmzed Naïve Bayes SMS Spam flterng algorthm, the man purpose s to reduce the consumpton of hardware resources and reflect user s ndvdual preference. A feature lbrary s desgned to save weght of features, condtonal probabltes and pror probabltes. The flterng algorthm s dvded nto a feature lbrary updatng algorthm and an onlne SMS flterng algorthm. The feature lbrary s updated regularly by the feature lbrary updatng algorthm. The feature lbrary updatng algorthm does not need to run mmedately, and can be performed n free tme of moble applcaton or coped to PC clent asynchronously. The onlne SMS flterng algorthm runnng on a moble phone performs only the mnmum amount of wor whch does not requre much resources. It can be qucly adapted to the changng of user s preference. Expermental results show that the algorthm can mprove the speed of classfcaton whle mantanng a hgh classfcaton accuracy. t taes up less storage space and can be used on common moble platforms. Keywords: feature lbrary, Naïve Bayes, onlne SMS spam flterng algorthm, short messages, updatng algorthm 1 Introducton Wth the development of wreless communcaton, the number of moble phone users n the world ncreased rapdly. Accordng to the Forrester s forecasts, moble phone users wll reach 48 bllon n the world by the end of 2016 [1]. Short Message Servce (SMS) s a popular means of moble communcaton. Due to huge success of Short Message Servce, SMS has now become the preferred [2] and cheapest medum for advertsers to reach masses especally n developng countres le Chna. SMS traffc has brought huge economc losses [3]. In August 2015, Badu moble guards released the frst half of 2015, Chna Moble Internet Securty Report [4]. Accordng to the report, n the frst half of 2015, advertsng s the largest nd of spam messages, accountng for 30% of the total, followed by telecommuncatons busness promoton, fnancal Servces, news broadcasts, etc. Spam can be defned as unsolcted emal for a recpent or any messages that the users do not wanted to have n ther nbox. The desgn of current moble phone nbox dd not provde flexblty to delete a spam SMS wthout loong at t. Due to unnecessary notfcatons and content, spam SMS has become a source of annoyance, a waste of tme and frequent dsturbance for the recever, and become a wdespread * Correspondng Author 174

2 Journal of Computers Vol. 28, No. 3, 2017 socal problem. Operators of short messages have no perfect spam message montorng system so far. They stll use spam flterng methods. SMS spam flters generally use four technques: Blac and whte lst flterng, eyword matchng flterng, flterng based on rules and content based SMS spam flterng. The frst three technques are smple, effcent and easy to mplement and requre lttle computng resources [5]. Blac and whte lst flterng, eyword matchng flterng all need manual settng of phone numbers or ey words, such as Free, Account, etc. Users preset telephone numbers of spammers and eywords frequently appear n spam messages. Thus, f a short message comes from those telephone numbers or contans those eywords, t wll be saved nto the spam folder and the user wll not be alerted by ths spam message. Furthermore, Manual updatng of blac lst s always later than the emergence of new SMS spam senders. The SMS spam senders wll feel the rule. They are very wea when t comes to ntentonal modfcaton of eywords or phone numbers. Rule-based flterng has the advantages of smple, fast, and needng no tranng phases, but fxed rules can t adapt to the new characterstcs of spam messages. If the user s nterest changes or the categores of spam vary, these rules need to be changed accordngly. So the accuracy rate of these flters are often poor. On the other hand, content based SMS spam flterng technques show better accuracy results compared to other methods. However, theses methods requre much more computaton resources. At present, studes on SMS spam flterng are manly focused on content based spam flterng algorthms. Another ssue needng to be concerned about s the dfference between short messages and e- mal. Short messages are short and lac of characterstc data whch are dfferent from emals. A emal conssts of certan structured nformaton such as subject, mal header, sender s address etc, but a short message does not have such structured nformaton and often conssts of few words, whch brng dffcultes to SMS spam flterng. The tradtonal content based spam flterng algorthms cannot be drectly appled to SMS spam flterng, feature selecton s partcularly mportant [6]. It s necessary to develop an effcent SMS Spam flterng method whch classfy short messages nto spam and non-spam SMS effcently. The scentfc contrbutons of ths research wor nclude: (1) Desgnnng SMS feature lbrary, the content-based SMS spam flterng algorthm s dvded nto feature lbrary updatng algorthm and real-tme SMS classfcaton algorthm. (2) The feature lbrary updatng algorthm adopts the method of asynchronous updatng, whch reduces the amount of calculaton of clent real-tme processng. (3) Improvng the Nave Bayesan classfcaton algorthm, and appled to real-tme SMS flterng, t can mmedately adapt to the changng content of short messages and flterng needs of the user. The rest of the paper s organzed as follows. Secton II present the related wor on the SMS Spam flterng. Secton III specfes the desgn and mplementaton of the system ncludng the system archtecture, feature lbrary, updatng algorthm and SMS classfcaton algorthm. The experments, testng and results are reported and dscussed n Secton IV. Fnally, the concluson s presented n Secton V. 2 Related Wor 2.1 Content Based SMS Spam Flterng Content based spam flterng algorthms nclude artfcal mmune [7], support vector machne, decson tree [8], Neural Networs and Naïve Bayes algorthm. The mmune system s a complex networ of organs and cells whch s responsble for the organsm s defense aganst alen partcles. The capacty of dstngushng between self and nonself genes s the man features of the mmune system. Support Vector Machnes s a lnear maxmal margn bnary classfer. It produces (lnear) vectors that try to maxmally separate the target classes (spam versus legtmate). It has shown excellent results n text classfcaton applcatons. Decson tree s a classfer n the form of a tree structure to show the reasonng process. Each node n decson tree structures s ether a leaf node or a decson node. Artfcal Neural Networs s also a wdely used machne learnng based flter whch are computer models that try to smulate the same tass that the bran carres out [9]. Naïve Bayes classfer s a smple probablstc classfer. The man advantage s that naïve Bayes classfers can be traned very effcently n a supervsed learnng. Al-Hasan and El-Alfy [10] developed a novel approach based on DCA that fuses the output from two 175

3 Optmzng Naïve Bayes Algorthm for SMS Spam Flterng on Moble Phone to Reduce the Consumpton of Resources classfers. The emprcal results showed sgnfcant mprovement can be acheved when applyng the proposed approach. Joe [11] proposed a SVM spam flter model whch selects meanngful features among thousands of orgnal ones by ch-square statstcs after preprocessng. Healy et al. [12] studed the problems of performng spam classfcaton on short messages by comparng the performance of the well-nown K-Nearest-Neghbor (KNN), Support Vector Machnes (SVM), and Nave Bayes classfers. They conclude that, for short messages, the SVM and Naïve Bayes classfers substantally outperform the KNN classfer. Hdalgo et al. [13] also accomplshed content flterng experments on Englsh and Spansh spam SMS corpora, whch proved that Bayesan flterng methods are stll effectve aganst spam SMS messages. Shah [14] also beleves that Naïve Bayes and Support Vector Machnes (SVM) based classfcatons are the two most successful technques. These methods all requre tranng samples, but because of prvacy ssues nvolved, lac of tranng sample lbrary s a major problem at present, whch has brought dffcultes to researches on SMS spam flterng [15]. In addton, for exstng two categores-based Bayesan spam flterng, f a user changes the category of some messages, the pror probablty of spam messages and normal SMS, the class condtonal probablty of features all need to be recalculated, whch wll ncrease the burden of clent processng. In ths paper, a feature lbrary s desgned and on whch an optmzed Naïve Bayes algorthm s proposed. 2.2 The Weanesses of Earler Study The flterng algorthm made no consderaton of moble phone s hardware resources. Moble phone s a embedded system wth lmted hardware resources. It brng dffcultes to run complex SMS spam flterng system. Most of prevous researches dd not consder the actual runnng envronment and the algorthm testng were conducted on personal computers. They only focused on the accuracy, precson and recall of spam flters from the vew pont of the algorthm used [10, 14], but a spam flter on a moble phone must consder the consumpton of resources, because energy, memory and computng ablty all are lmted. The flterng algorthm dd not tae nto account the user s ndvdualzed requrements. All users are gven the same classfcaton results [16]. Due to the specal nature of SMS, everyone has dfferent opnon. Frst of all, dfferent users gve dfferent classfcaton results to the same message, for example, a real estate sales based SMS s an mportant message to users nterested n buyng houses, but others may consder t to be a spam message. Secondly, user s preferences change over tme. At one stage, a message s consdered as a spam message. At another stage, t may be determned to be a normal SMS. 2.3 User s Expectatons from a SMS Spam Flterng Soluton From the vew of mplementaton, there are two nds of technologcal solutons used for SMS spam flterng, whch nclude Short Message Servce Center (SMSC) based flterng and flter by nstallng related software n the moble phone [17-18]. For SMSC based flterng, all the spam SMSes wll be fltered by moble operators. Once the classfcaton error occurs at the servce center, a normal message wll be lost and could not reach the moble phone user. It also nvolves the nterests of the operators, so ths nd of flter s not easy to mplement. In order to understand users requrements, behavor and perceptons related to spam SMS, Yadav et al. [19] conducted a survey from 458 partcpants of dfferent age groups and ctes n Inda, the result show that there s a need for personalzed SMS spam flterng whch taes nto account wth user preferences and nformaton requrements. Due to neffectveness of avalable solutons, moble users showed strong wllngness to use a technologcal soluton for SMS spam flterng. The purpose of ths paper s to propose an optmzed Naïve Bayes classfer for SMS Spam flterng on moble devces n order to reduce the consumpton of resources, that means the flter can run on common moble termnals wth lmted energy, memory and computng ablty. Even more, we expect our approach to bloc moble spam messages and have hgher accuracy and operatng effcency. The flter can also adapted to the preference of ndvduals. The ey research problem of ths paper ncludes: (1) How to desgn the feature lbrary. (2) How to update the feature lbrary wthout affectng the effcency of flterng system. (3) How to desgn the real-tme SMS spam flterng algorthm. 176

4 Journal of Computers Vol. 28, No. 3, Proposed System 3.1 System descrpton Ths paper proposed an optmzed Naïve Bayes classfer for SMS Spam Flterng on moble phones for the purpose of reducng the consumpton of resources. As shown n Fg. 1, the flterng system conssts of feature lbrary, feature lbrary updatng algorthm and onlne SMS spam flterng algorthm. The feature lbrary s generated on the PC sde by a large number of tranng samples after beng preprocessed and selected. The feature lbrary s updated regularly by the feature lbrary updatng algorthm, and the updatng cycle can be set by users. The feature lbrary updatng algorthm does not need to perform onlne, but performs n free tme of moble applcaton or be coped to PC sde asynchronously. Features of a new short message are sent to onlne SMS spam flterng algorthm. Meanwhle, the user can get real-tme classfcaton results. Fg. 1. System confguraton The updatng algorthm taes on the more complex and tme consumng jobs, such as computng and updatng of condtonal probabltes, whle the onlne SMS spam flterng algorthm performs only the mnmum amount of wor for flterng whch gets the data from the feature lbrary. The onlne SMS flterng algorthm requres mnmal resources whch can run on moble phones and can adapt tself qucly to the changng of the user s preference. 3.2 Features of Proposed System Tang up less moble storage capacty. After preprocessng and feature extracton, removng the SMS message s nterference and rrelevant nformaton, the feature lbrary only store the features and the number of feature occurrence n the sample database, not the actual content of short messages, whch can reduce the storage capacty. The feature lbrary can also be shared between flterng systems. Consumng less computng resources. Although the number of tranng samples ncreases, the amount of wor that a moble phone has to deal wth should almost be lttle. The flter consumes less tme and memory capacty, whch can be used on moble phones wth less computng and storage resources, that means the flter on the moble phone s free from burden of tranng process. Classfyng SMS messages accordng to user s preferences. Ths paper establshes feature lbrary on moble phone sde, the flterng system classfy short messages accordng to user s preferences to avod the lmtatons of tradtonal flterng system by whch all users are gven the same classfcaton results. 177

5 Optmzng Naïve Bayes Algorthm for SMS Spam Flterng on Moble Phone to Reduce the Consumpton of Resources 4 Implementaton 4.1 Feature Selecton Snce havng a good feature representaton s one of the most mportant parts for gettng a good classfer, we have to face the fact that SMS messages don t have the same structure and characterstcs than emal messages, and that could be a problem because fewer words extracted from SMS means less nformaton to wor wth. Many feature selecton technques are used n the area of text classfcaton such as mutual nformaton, CHI statstcs, Informaton Gan, Term strength, document frequency, etc. The purpose of feature selecton n ths paper s to select a subset of features from all the features n SMS samples whch should contan as much classfcaton nformaton as possble. Some of the features appear rarely n short messages, but they are very mportant for classfcaton, such as gun. Some of the features appear frequently n all short messages, such as good, but t does lttle to classfcaton. Mutual nformaton functon descrbes the relevance of a feature to a category. In SMS classfcaton, f the value of mutual nformaton functon of feature t to categores C s hgh, that s not to say the value of mutual nformaton functon of feature t to categores C j s low. So f t frequently appears n multple classes, ts effect to the classfcaton result s lttle. It even produces the nterference n the process of probablty calculaton, whch wll reduce the flter s performance. In ths paper, the mutual nformaton functon s mproved, If the feature t s frequently appear n only one category, t has a hgher weght value. wt ( ) s the value of feat_val. It s calculated accordng to the formula (1), (2), and (3). n 2 = = = 1 avg (1) w( t ) ( MI( t, C ) MI ( t )) Pt ( C) MI( t, C ) = log2 Pt ( ) PC ( ) (2) n MI ( t ) = P( C ) MI ( t, C ) (3) avg = 1 Pt ( ) represents the quotent whch the occurence of t belongs to the occurence of all features. PC ( ) represents the quotent whch the number of messages belong to type C dvde the the number of messages of the tranng set. It can be calculated accordng to the formula (4). The onlne SMS spam flterng algorthm reads ts value from table SMS_category. Pt ( C ) represents the quotent whch the number of occurrences of feature t n class C dvde the number of occurrences of all features n class C ( t s the probablty of term t gven presence of class C ), the value of t can be read form table Feature_cat. wt ( ) s the weght of feature t, t descrbes the ablty of feature t to dstngush C from other categores. Some of the features s selected and nserted nto the feature lbrary accordng to the value of wt ( ). The larger value of wt ( ), the hgher prorty for beng selected. 4.2 Desgn of the Feature Lbrary In order to reduce the CPU consumpton on the executon of SMS spam flterng algorthm and reduce memory capacty occuped by characterstc data of SMS samples, ths paper desgns the feature lbrary to save the characterstc data of the SMS samples, whch nclude SMS categores table SMS_category, feature nformaton table Feature_nfo and feature categores table Feature_cat. SMS_category (cat_d, category, samp_nu, flag, PC). Cat_d s an dentfer of a SMS category, and s the prmary ey of table SMS_category. Category s the name of a SMS category. Short messages can be set by users nto dfferent categores. For example, messages are dvded nto categores of merchandsng, pornographc and llegal nformaton, daly greetngs, busness communcaton, real 178

6 Journal of Computers Vol. 28, No. 3, 2017 estate sales, nsurance. Wheren merchandsng, pornographc and llegal nformaton categores are of spam messages. Daly greetngs and busness communcaton categores are of normal messages. Real estate sales and nsurance categores can be set as normal messages or spam messages by users accordng to ther preferences. Samp_nu s the number of the samples belong to the SMS category dentfed by the cat_d. Flag represents whether ths category s spam messages, where 1 represents the spam category and 0 represents the normal category. PC s the pror probablty of type C. PC ( ) s the value of PC whch can be calculated accordng to the formula (4). If a user changes the flag attrbute of a SMS category, the probablty of a message belongng to that category does not need to change accordngly, so does other relevant data n the feature lbrary, they can be automatcally updated. SMS spam flterng system can also adapt to the new category settngs on real-tme. SC PC ( ) = (4) S Where SC K s the number of messages of class C. S s the number of all the messages. Feature_nfo(feat_d, feat, feat_val, rec_tme). Feat_d s the dentfer of a feature, and s the prmary ey of table Feature_nfo. Feat s the name of the feature dentfed by the feat_d and feat_val s ts weght value of classfcaton. Rec_tme records the last tme of the feature beng accessed by the on lne SMS spam flterng system. In order to control growth rate of the storage space occuped by the feature lbrary and reduce the storage burden on the clent, ths artcle remove features from the feature lbrary perodcally accordng to the value of rec_tme. Feature_cat(feat_d, C, feat_nu, feat_c). Feat_d s the dentfer of a feature. C s the dentfer of a SMS category. Feat_nu s the number of tmes a feature characterzed by feat_d appears n short messages of class CK. Feat_C s the probablty the feature appears n short messages of CK class. The value of feat_c s Pt ( C) whch s calculated accordng to the formula (5). Feat_d reference feat_d property n table Feature_nfo. C reference cat_d property n table SMS_category. Word_d plus cat_d consttute the prmary ey of ths table. The value of feat_c s saved n the feature lbrary n order to avod the repeated calculaton durng the executon of the SMS spam flterng algorthm. It can be automatcally updated by the feature lbrary updatng algorthm descrbed n secton 4.2. N K Pt ( C) = N + m t _ c + 1 Nc K s the total number of occurrence of all features belong to C class. Nt _ c K s the number of occurrence of t belongs to C class, m s the total number of features n the feature lbrary and repeated features are calculated only once. 4.3 Feature Lbrary Updatng Algorthm The feature lbrary updatng algorthm s descrbed as follows: Algorthm FLUP(X) Input: New SMS features table X, feature lbrary L Output: Updated feature lbrary L Read category nformaton from X, update samp_nu, flag, PC( PC ( ) f class( csms ) = f class( csms ) C C ck ) n SMS_category table. Nc + 1 N 1 all PC ( ) = = PC ( ) + N + 1 N + 1 N + 1 ( C = C ) (6) all all all (5) Nc N all PC ( ) = PC ( ) N + 1 = N + 1 all all ( C C ) (7) 179

7 Optmzng Naïve Bayes Algorthm for SMS Spam Flterng on Moble Phone to Reduce the Consumpton of Resources For each feature n X: f t exsts n the Feature_nfo table then update feat_val n Feature_nfo table; update feat_nu, feat_c( Pt ( C ) ) n Feature_cat table; f class( csms ) = C and t csms Nt _ c + N K t_ csms Pt ( C) = ( C = C 並且 t csms ) Nc + N K all_ csms Nc N t _ csms = Pt ( C) + N + N N + N c all _ csms c all _ csms (8) 180 f class( csms ) = C and t csms N Pt ( C) Pt ( C) c = Nc + N all _ csms ( C C = 並且 t csms ) (9) else nset t nto Feature_nfo table and Feature_cat table; If the number of features Nall s greater than N max Remove N features of lowest weght value from Feature_nfo table n L; Cascadng delete the data from Feature_cat table. Csms s a newly classfed short message watng to be nserted nto the feature lbrary. s the number of the features of class C. Nall s the total number of the features n the feature lbrary. Nt _ csms s the number of occurrences of t n csms. 4.4 Classfcaton Algorthm N all _ csms Nc s the number of occurrences of all features n csms. The steps of optmzed Naïve Bayes onlne SMS spam flterng algorthm s as follows. Algorthm IMNB(nsms) Input: a new short message nsms, feature lbrary L Output:the category of nsms(spam message or normal message) Extract the feature t 1 t 2... t n from nsms For each C = cat_d n SMS_category table: Read PC ( ), Pt ( C) from L, compute PC ( nsms) = n PC ( ) Pt ( C) Fnd C, C where j = 1 n PC ( ) Pt ( C) = 1 P( C nsms) = arg max P( C nsms) and cat_ d&& cat_ d PC ( nsms) = arg max PcC nsms) j If ( C nospam and Cj nospam) The class of nsms s nospam; If ( C spam and Cj spam) The class of nsms s spam; If (( C spam and Cj nospam)&&( PC ( nsms )/ PC ( j nsms ))>R) The class of nsms s spam; else the class of nsms s nospam; If ( C nospam and Cj spam) The class of nsms s nospam.

8 Journal of Computers Vol. 28, No. 3, 2017 Nsms s a new short message watng to be classfed. n s the number of features extracted from the nsms. 5 Experments and Analyss 5.1 Expermental Envronment The experment use Tny210 development board as the hardware platform whch s based on ARM CortexTM-A8. Samsung S5PV210 s the man processor. It s runnng speed can reach 1 GHZ, ts memory s 512 MB. The SMS spam flterng algorthm s runnng on the embedded Lnux operatng system, SQLte s used as the embedded database to save the feature lbrary. The host machne nstall Lnux operatng system and arm-lnux-gcc cross complaton envronment. 5.2 Testng of the Feature Lbrary sze Because of lmted storage capacty and processng power, t s mportant to nown the storage space occuped by the feature lbrary. Ths experment test the sze of feature lbrary fle durng the ncreasement of features. The results are shown n Fg. 2. When the number of features s 25, the sze of feature lbrary fle s 5120 bytes. When the number of features s 54, the sze of feature lbrary fle s 6003 bytes. When the number of features s 82, the sze of feature lbrary fle s 7168 bytes. When the number of features reaches 170, the sze of feature lbrary fle s up to bytes. If 10 features are extracted from each of short messages, the growth of the feature lbrary fle s about bytes at every ncrease of one SMS sample. When 2000 short messages are nserted nto the feature lbrary, the fle sze of t s about K. It s completely feasble to run on the general moble platforms. Ths s acceptable because t can completely run on a moble platform even the cheapest moble phones. 5.3 Testng of the Accuracy Rate of the Algorthm Fg. 2. The feature lbrary sze versus feature number There s no publc corpus for Chnese SMS spam flterng. The experment uses the SMS samples collected by ourselves, ncludng 706 spam messages and 813 normal messages. The SMS samples s dvded nto tranng set and testng set. The tranng set ncludes 400 spam messages and 430 normal messages. The testng set conssts of 306 spam messages and 383 normal messages. Ths artcle uses an evaluaton ndex wdely used n spam flterng. The flterng accuracy s calculated by the formula (10). OA = ( TP + TN) ( TP + FP + FN + TN ) TP s the number of spam messages correctly classfed. TN s the number of normal messages correctly classfed. FP s the number of normal messages msclassfed as spam messages. (10) 181

9 Optmzng Naïve Bayes Algorthm for SMS Spam Flterng on Moble Phone to Reduce the Consumpton of Resources FN s the number of spam messages msclassfed as normal SMS. The accuracy rate of the optmzed Naïve Bayes SMS spam flterng algorthm proposed n ths paper s tested. The result s shown n Fg. 3. When the number of the features extracted from each of messages s between 9 to 13, the average accuracy rates of the algorthm are all over 90%, the accuracy rate reaches a maxmum when the number of features s 11. When the number of features s more than 13, the accuracy rate shows a downward trend, whch means that features wth low weght value reduce the performance of the flterng algorthm. Fg. 3. Accuracy rate vs. number of features 5.4 Testng of Tme Performance of the Flterng System The 689 short messages n the testng set are classfed usng the algorthm proposed n ths paper. The classfcaton tme of each message s recorded. Its value range from 0.05 seconds to 0.18 seconds, wth an average tme of 0.1 seconds. For comparson, the tradtonal Bayesan algorthm s also tested on tny210 platform, the average classfcaton tme of a messages s 1.1 seconds. Because the mproved algorthm omts a lot of calculaton, the processng speed s greatly mproved. We gnore the runnng tme of the feature lbrary updatng algorthm because t s assumed that the updatng could happen offlne and hence the tme spent by the updatng algorthm s not relevant. 6 Concluson and Future Wor Ths paper proposed a SMS spam flterng algorthm that requres mnmal resources and reflects user s ndvdual preference on an ndependent moble phone whch does not depend on another server or moble operators. It obtaned the reasonable accuracy, low storage consumpton and quc flterng speed. In addton to these, our approach ensures securty and prvacy because the flterng algorthm runs on a moble phone and each user has ts own feature lbrary, spammers have no way to access to the feature lbrary. The lmtatons of the algorthm proposed n ths paper s that t apples only to the text nformaton flterng. Feature selecton plays a very mportant role n short message classfcaton, the more effectve feature extracton and feature selecton approaches reman as nterestng future wors. Acnowledgement The research s supported by hgher school scentfc research project of Gansu Provnce (Grant No.2013A-127). The authors would le to than the Assocate Edtor and the revewers for ther valuable comments. References [1] Yesy YORK News, The global moble phone users wll amount to 4.8 bllon by the end of 2016, < 182

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