Efficient Spam Classification by Appropriate Feature Selection

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1 Global Journal of Computer Science and Technology Software & Data Engineering Volume 13 Issue 5 Version 1.0 Year 2013 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: & Print ISSN: Efficient Spam Classification by Appropriate Feature Selection By Prajakta Ozarkar & Dr. Manasi Patwardhan Vishwakarma Institute of Technology, India Abstract - Spam is a key problem in electronic communication, including large-scale systems and the growing number of blogs. Currently a lot of research work is performed on automatic detection of spam s using classification techniques such as SVM, NB, MLP, KNN, ID3, J48, Random Tree, etc. For spam dataset it is possible to have large number of training instances. Based on this fact, we have made use of Random Forest and Partial Decision Trees algorithms to classify spam vs. non-spam s. These algorithms outperformed the previously implemented algorithms in terms of accuracy and time complexity. As a preprocessing step we have used feature selection methods such as Chi-square, Information gain, Gain ratio, Symmetrical uncertainty, Relief, One R and Correlation. This allowed us to select subset of relevant, non redundant and most contributing features to have an added benefit in terms of improvisation in accuracy and reduced time complexity. Keywords : feature selection, preprocessing, random forest, part. GJCST-C Classification : H.4.3 Efficient Spam Classification by Appropriate Feature Selection Strictly as per the compliance and regulations of: Prajakta Ozarkar & Dr. Manasi Patwardhan. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License permitting all noncommercial use, distribution, and reproduction inany medium, provided the original work is properly cited.

2 Efficient Spam Classification by Appropriate Feature Selection Prajakta Ozarkar α & Dr. Manasi Patwardhan σ Abstract - Spam is a key problem in electronic communication, including large-scale systems and the growing number of blogs. Currently a lot of research work is performed on automatic detection of spam s using classification techniques such as SVM, NB, MLP, KNN, ID3, J48, Random Tree, etc. For spam dataset it is possible to have large number of training instances. Based on this fact, we have made use of Random Forest and Partial Decision Trees algorithms to classify spam vs. non-spam s. These algorithms outperformed the previously implemented algorithms in terms of accuracy and time complexity. As a preprocessing step we have used feature selection methods such as Chi-square, Information gain, Gain ratio, Symmetrical uncertainty, Relief, One R and Correlation. This allowed us to select subset of relevant, non redundant and most contributing features to have an added benefit in terms of improvisation in accuracy and reduced time complexity. Keywords : feature selection, preprocessing, random forest, part. I. Introduction I n this paper we have studied previous approaches used for classifying spam and non spam s by using distinct classification algorithms. We have also studied the distinct features extracted for classifier training and the feature selection algorithms applied to get rid of irrelevant features and selecting the most contributing features. After studying the current feature selection and classification approaches, we have applied two new classification techniques viz. Random forests and Partial decision trees along with distinct feature selection algorithms. R.Parimala,et.al. [1] Presents a new (Feature selection) technique which is guided by F selector Package. They have used nine feature selection techniques such as Correlation based feature selection, Chi-square, Entropy, Information Gain, Gain Ratio, Mutual Information, Symmetrical Uncertainty, One R, Relief and five classification algorithms such as Linear Discriminant Analysis, Random Forest, Rpart, Naïve Byes and Support Vector Machine on spam base dataset. In their evaluation, the results show that filter methods C, Chi-squared, GR, Relief, SU, IG, and one Author α : Prajakta Ozarkar, Student, Vishwakarma Institute of Technology, Pune, Maharashtra, India. Author σ : Manasi Patwardhan, Professor, Vishwakarma Institute of Technology, Pune, Maharashtra, India. Enables the classifiers to achieve the highest increase in classification accuracy.they conclude that the implemented can improve the accuracy of Support vector machine classifiers by performing. In the paper by R. Kishore Kumar, et.al.[2] spam dataset is analyzed using Tanagra data mining tool. Initially, feature construction and feature selection is done to extract the relevant features by using Fisher filtering, Relief, Runs Filtering, Step disc. Then classification algorithms such as C4.5, C-PLS, C-RT, CS-CRT, CS-MC4, CS-SVC, ID, K-NN LDA, Log Reg TRIRLS, Multilayer Perceptron, Multilogical Logistic Regression, Naïve Bayes Continuous, PLS-DA, PLS- LDA, Rend Tree and SVM are applied over spam base dataset and cross validation is done for each of these classifiers. They conclude Fisher filtering and Runs filtering feature selection algorithms performs better for many classifiers. The Rend tree classification algorithm with the relevant features extracted by fisher filtering produces more than 99% accuracy in spam detection. W.A. Awad, et.al. [3] reviews machine learning methods Bayesian classification, k-nn, ANNs, SVMs, Artificial immune system and Rough sets on the Spam Assassin spam corpus. They conclude Naïve bayes method has the highest precision among the six algorithms while the k-nearest neighbor has the worst precision percentage. Also, the rough sets method has a very competitive percentage. In the work by V.Christina, et.al.[4]employs supervised machine learning techniques namely C4.5 Decision tree classifier, Multilayer perceptron and Naïve Bayes classifier. Five features of an all (A), header (H), body (B), subject (S), and body with subject (B+S), are used to evaluate the performance of four machine learning algorithms. The training dataset, spam and legitimate message corpus is generated from the mails that they have received from their institute mail server for a period of six months. They conclude Multilayer Perceptron classifier outperforms other classifiers and the false positive rate is also very low compared to other algorithms. Rafiqul Islam, et.al. [5] have presented an effective and efficient classification technique based on data filtering method. In their testing they have introduced an innovative filtering technique using instance selection method (ISM) to reduce the pointless data instances from training model and then classify the test data. In their model, tokenization and domain 49

3 50 2 specific feature selection methods are used for feature extraction. The behavioral features are also included for improving performance, especially for reducing false positive (FP) problems. The behavioral features include the frequency of sending/receiving s, attachment, type of attachment, and size of attachment and length of the . In their experiment, they have tested five base classifiers Naive Bayes, SVM, IB1, Decision Table and Random Forest on 6 different datasets. They also have tested adaptive boosting (AdaboostM1) as meta-classifier on top of base classifiers. They have achieved overall classification accuracy above 97%. A comparative analysis is performed by Ms.D KarthikaRenuka, et.al. [6], for the classification techniques such as MLP, J48 and Naïve Bayesian, for classifying spam messages from using WEKA tool. The dataset gathered from UCI repository had 2788 legitimate and 1813 spam s received during a period of several months. Using this dataset as a training dataset, models are built for classification algorithms. The study reveals that the same classifier performed dissimilarly when run on the same dataset but using different software tools. Thus, from all perspectives MLP is top performer in all cases and thus, can be deemed consistent. Following table summarizes all the previous classification approaches enlisted above and provides a comparison in terms of % accuracy they have achieved with the application of a specific feature selection algorithm. Table 1 : Comparison of previous approaches of spam detection Reference R.Parimala,et.al. [1] R. Kishore Kumar, et.al.[2] Classifier Used and features % Feature Selection Acc SVM (100%) - 93 SVM (16%) C SVM (70%) Chi SVM (70%) IG SVM (70%) GR SVM (70%) SU SVM (70%) oner SVM (70%) Relief SVM( 32%) Lda SVM (12%) Rpart SVM (16%) SVM SVM (21%) RF SVM (7%) NB C-PLS Fisher C-RT Fisher CS-CRT Fisher CS-MC4 Fisher CS-SVC Fisher ID3 Fisher KNN Fisher LDA Fisher LogReg TRI Fisher Reference R. Kishore Kumar, et.al.[2] W. A. Awad, et.al.[3] II. Classifier Used and features % Feature Selection Acc MLP Fisher Multilogical LR Fisher NBC Fisher PLS-DA Fisher PLD-LDA Fisher Rnd Tree Fisher SVM Fisher C4.5 Relief C-PLS Relief C-RT Relief CS-CRT Relief CS-MC4 Relief CS-SVC Relief ID3 Relief KNN Relief LDA Relief LogReg TRI Relief MLP Relief Multilogical LR Relief NBC Relief PLS-DA Relief PLD-LDA Relief Rnd Tree Relief SVM Relief C4.5 Runs C-PLS Runs C-RT Runs CS-CRT Runs CS-MC4 Runs CS-SVC Runs ID3 Runs KNN Runs LDA Runs MLP Runs LogReg TRI Runs Multilogical LR Runs NBC Runs PLS-DA Runs PLD-LDA Runs Rnd Tree Runs SVM Runs C4.5 StepDisc C-PLS StepDisc C-RT StepDisc CS-CRT StepDisc CS-MC4 StepDisc CS-SVC StepDisc ID3 StepDisc KNN StepDisc LDA StepDisc LogReg TRI StepDisc MLP StepDisc Multilogical LR StepDisc NBC StepDisc PLS-DA StepDisc PLD-LDA StepDisc Rnd Tree StepDisc SVM StepDisc NBC SVM KNN ANN AIS Rough Sets V.Christina, et.al.[4] NBC J MLP RafiqulIslam,et.al [5] Ms.DKarthikaRenuk a,et.al [6] NB SMO IB DT RF MLP - 93 J48-92 NBC - 89 Proposed Work After a detailed review of the existing techniques used for spam detection, in this section we are illustrating the methodology and techniques we used for spam mail detection.

4 Figure 1 shows the process we have used for spam mail identification and how it is used in conjunction with a machine learning scheme. Feature ranking techniques such as Chi-square, Information gain, Gain ratio, Symmetrical uncertainty, Relief, One and Correlation are applied to a copy of the training data. After the feature selection subset with the highest merit is used to reduce the dimensionality of both the original training data and the testing data. Both reduced datasets may then be passed to a machine learning scheme for training and testing. Results are obtained by using Random Forest and Part classification techniques. Figure 1 : Stages of Spam Classification In the following subsections we discuss the basic concepts related to our work. It includes a brief background on feature ranking techniques, classification techniques and results. III. Data Set The dataset used for our experiment is spam base [13].The last column of 'spam base. Data' denotes whether the was considered spam (1) or not (0). Most of the attributes indicate the frequency of spam related term occurrences. The first 48 set of attributes (1 48) give tf-idf (term frequency and inverse document frequency) values for spam related words, whereas the next 6 attributes (49-54) provide tf-idf values for spam related terms. The run-length attributes (55-57) measure the length of sequences of consecutive capital letters, capital_ run_ length_ average, capital_ run_ length_ longest and capital_ run_ length_ total. Thus, our dataset has in total 57 attributes serving as an input features for spam detection and the last attribute represent the class (spam/non-spam). We have also used one public dataset Enron [20].The preprocessed subdirectory contains the messages in the preprocessed format. Each message is in a separate text file. The body of an contains the actual information. This information needs to be extracted before running a filter process by means of preprocessing. The purpose for preprocessing is to transform messages in mail into a uniform format that can be understood by the learning algorithm. Following are the steps involved in preprocessing: 1. Feature extraction (Tokenization): Extracting features from in to a vector space. 2. Stemming: Stemming is a process for removing the commoner morphological and in-flexional endings from words in English. 3. Stop word removal: Removal of non-informative words. 4. Noise removal: Removal of obscure text or symbols from features. 5. Representation: tf-idf is a statistical measure used to calculate how significant a word is to a document in a feature corpus. Word frequency is established by term frequency (tf), number of times the word appears in the message yields the significance of the word to the document. The term frequency then is multiplied with inverse document frequency (idf) which measures the frequency of the word occurring in all messages IV. Feature Ranking and Subset Selection From the above defined feature vector of total 58 features, we use feature ranking and selection algorithms to select the subset of features. We rank the given set of features using the following distinct approaches. a) Chisquare Chi-squared hypothesis tests may be performed on contingency tables in order to decide whether or not effects are present. Effects in a contingency table are defined as relationships between the row and column variables; that is, are the levels of the row variable differentially distributed over levels of the column variables. Significance in this hypothesis test means that interpretation of the cell frequencies is warranted. Non-significance means that any differences in cell frequencies could be explained by chance. Hypothesis tests on contingency tables are based on a statistic called Chi-square [8]. 2 (O E) 2 = E Where, O Observed cell frequency, E Expected cell frequency. b) Information Gain Information Gain is the expected reduction in entropy caused by partitioning the examples according to a given attribute. Information gain is a symmetrical 51

5 52 2 measure that is, the amount of information gained about Y after observing X is equal to the amount of information gained about X after observing Y. The entropy of Y is given by [9] HH YY = PP YY lloogg2(pp YY )yy YY If the observed values of Y in the training data are partitioned according to the values of a second feature X, and the entropy of Y with respect to the partitions induced by X is less than the entropy of Y prior to partitioning, then there is a relationship between features Y and X. Equation gives the entropy of Y after observing X HH YY = (xx) PP yy xx lloogg2(pp yy xx )yy YYxx XX The amount by which the entropy of Y decreases reflects additional information about Y provided by X and is called the information gain or alternatively, mutual information [9]. Information gain is given by GGaaiinn=HH YY + HH YY XX = HH XX + HH XX YY =H Y +H X (XX,YY) c) Gain Ratio The various selection criteria have been compared empirically in a series of experiments. When all attributes are binary, the gain ratio criterion has been found to give considerably smaller decision trees. When the task includes attributes with large numbers of values, the subset criterion gives smaller decision trees that also have better predictive performance, but can require much more computation. However, when these many-valued attributes are augmented by redundant attributes which contain the same information at a lower level of detail, the gain ratio criterion gives decision trees with the greatest predictive accuracy. All in all, it suggests that the gain ratio criterion does pick a good attribute for the root of the tree [12]. GGaaiinn RRaattiioo=HH YY +HH XX HH(YY,XX)HH(XX) d) Symmetrical Uncertainty Information gain is a symmetrical measure that is, the amount of information gained about Y after observing X is equal to the amount of information gained about X after observing Y. Symmetry is a desirable property for a measure of feature-feature inter correlation to have. Unfortunately, information gain is biased in favor of features with more values. Symmetrical uncertainty compensates for information gain s bias toward attributes with more values and normalizes its value to the range [0, 1] [9]: SSyymmmmeettrriiccaall UUnncceerrttaaiinnttyy CCooeeffff= 2.0 GGaaiinnHH YY +(XX) e) Relief Relief [10] is a feature weighting algorithm that is sensitive to feature interactions. Relief attempts to approximate the following difference of probabilities for the weight of a feature X [9]: WWXX=PP( ddiiffffeerreenntt vvaalluuee ooff XX nneeaarreesstt iinnssttaannccee ooff ddiiffffeerreenntt ccllaassss) PP (ddiiffffeerreenntt vvaalluuee ooff XX nneeaarreesstt iinnssttaannccee ooff ssaammee ccllaassss) By removing the context sensitivity provided by the nearest instance condition, attributes are treated as independent of one another; RReelliieeffXX= PP (ddiiffffeerreenntt vvaalluuee ooff XX ddiiffffeerreenntt ccllaassss) PP( ddiiffffeerreenntt vvaalluuee ooff XX ssaammee ccllaassss) Which can be reformulated as RReelliieeffxx= GGiinnii pp xx 2xx XX 1 pp cc 2cc CC pp cc 2cc CC Where, C is the class variable and GGiinnii = pp cc (1 pp) CC pp xx 2 pp xx 2xx XX pp cc xx (1 pp cc xx) cc CC xx XX 6. f) OneR Like other empirical learning methods, 1R [11] takes as input a set of examples, each with several attributes and a class. The aim is to infer a rule that predicts the class given the values of the attributes. The 1R algorithm chooses the most informative single attribute and bases the rule on this attribute alone. The basic idea is: For each attribute a, form a rule as follows: For each value v from the domain of a, Select the set of instances where a has value v. Let c be the most frequent class in that set. Add the following clause to the rule fora: if a has value v then the class is c Calculate the classification accuracy of this rule. Use the rule with the highest classification accuracy. The algorithm assumes that the attributes are discrete. If not, then they must be discretized. g) Correlation Feature selection for classification tasks in machine learning can be accomplished on the basis of correlation between features, and that such a feature selection procedure can be beneficial to common machine learning algorithms [9]. Features are relevant if their values vary systematically with category membership. In other words, a feature is useful if it is correlated with or predictive of the class; otherwise it is irrelevant. A good feature subset is one that contains features highly correlated with (predictive of) the class, yet uncorrelated with (not predictive of) each other. The acceptance of a feature will depend on the extent to which it predicts classes in areas of the instance space not already predicted by other features. Correlation

6 based feature selection feature subset evaluation function [9]: MMss=kkrrccff kk+kk kk 1 rrffff Where, - the heuristic merit of a feature subset S containing k features, rrccff-the mean feature-class correlation, rrffff -the average feature-feature intercorrelation. Feature ranking further help us to - 1. Remove irrelevant features, which might be misleading the classifier decreasing the classifier interpretability by reducing generalization by increasing over fitting. 2. Remove redundant features, which provide no additional information than the other set of features, unnecessarily decreasing the efficiency of the classifier. 3. Selecting high rank features, which may not affect much as far as improving precision and recall is concerned; but reduces time complexity drastically. Selection of such high rank features reduces the dimensionality feature space of the domain. It speeds up the classifier there of improving the performance and increasing the comprehensibility of the classification result. We have considered 87%, 77% and 70% of the features; wherein there is a performance improvement in 70% feature consideration. IV. Classification Method Based on the assumption that the given dataset has enough number of the training instances we have chosen the following two classification algorithms. The algorithms work well based on the fact that the dataset is of good quality. a) Random Forest Random Forests [14] are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Each tree is grown as follows: 1. If the number of cases in the training set is N, sample N cases at random - but with replacement, from the original data. This sample will be the training set for growing the tree. 2. If there are M input variables, a number m<<m is specified such that at each node, m variables are selected at random out of the M and the best split on these m is used to split the node. The value of m is held constant during the forest growing. 3. Each tree is grown to the largest extent possible. There is no pruning. Random Forest is an ensemble of trees. In our implementation of random forest we have selected a vector of 4 features (randomly selected), to build each tree in a forest of 10 random trees. Tree grows to its maximum depth as that argument is set to zero, which indicates unlimited depth. By using bagging and voting techniques classification is being done. For example, a sample part of the output of the forest (very small portion of the forest) is as shown below: Total Random forest Trees: 10 Numbers of random features: 4 Out of bag error: All the trees in the forest: RandomTree ========== word_freq_hpl < 0.07 char_freq_$ < 0.03 word_freq_you < 0.12 word_freq_hp < 0.02 char_freq_! < 0.01 word_freq_3d < 9.87 word_freq_000 < 0.08 char_freq_( < 0.04 word_freq_meeting < 0.85 word_freq_remove < 2.27 word_freq_free < 6.47 word_freq_will < 0.17 word_freq_pm < 0.42 word_freq_all < 0.21 word_freq_mail < 2.96 word_freq_re < 5.4 word_freq_technology < 1.43 capital_run_length_total < 18.5 word_freq_re < 0.68 word_freq_make < 1.39 capital_run_length_total < 10.5 : 0 (218/0) capital_run_length_total >= 10.5 word_freq_internet < 0.89 word_freq_people < 1.47 word_freq_data < 3.7 word_freq_edu < 2.38 char_freq_[ < 0.59 char_freq_; < 0.16 capital_run_length_total < 11.5 word_freq_credit < 9.09 : 0 (1/0) This is the case when 100%features have selected for training model, accordingly the root node of each tree changes. 53

7 54 2 b) Partial Decision Tree Rule learners are prominent representatives of supervised machine learning approaches. Basically, this type of learner tries to induce a set of rules for a collection of training instances. These rules are then applied on the test instances for classification purposes. Two well-known members of the family of rule-learners are C4.5 and RIPPER. C4.5 [16], for instance, generates an unprimed decision tree and transforms this tree into a set of rules. For each path from the root node to a leaf a rule is generated. Then, each rule is simplified separately followed by a rule-ranking strategy. Finally, the algorithm deletes rules from the rule set as long as the rule set s error rate on the training instances decreases. RIPPER [17] implements a divide and conquers strategy to rule induction. Only one rule is generated at a time and the instances from a training set covered by this rule are removed. It iteratively derives new rules for the remaining instances of the training set. PART (Partial Decision Trees) adopts the divide-and-conquer strategy of RIPPER [17] and combines it with the decision tree approach of C4.5 [16]. PART generates a set of rules according to the divide-and-conquer strategy, removes all instances from the training collection that are covered by this rule and proceeds recursively until no instance remains. To generate a single rule, PART builds a partial decision tree for the current set of instances and chooses the leaf with the largest coverage as the new rule. For example, following is the way of rule formation in our implementation of PART and some of the rules are as shown below: Rule 1: word_freq_remove> 0.0 AND char_freq_! > AND word_freq_edu<= 0.06: 1 (Instances: 490 and Incorrect: 7) Now, after Rule1 the next set of rules are formed excluding 490 instances from the 4601 total instances of spambase. Rule 2: char_freq_$ > AND word_freq_hp<=0.4 AND capital_run_length_longest> 9.0 AND word_freq_1999 <= 0.0 AND word_freq_edu<= 0.08 AND char_freq_! > 0.107: 1 (Instances: 334 and Incorrect: 2) Next set of rules is formed on remaining 3777 instnaces of spambase. Rule 3: word_freq_money<= 0.03 AND word_freq_000<=0.25and word_freq_remove<= 0.26 AND word_freq_free<=0.19and word_freq_font<= 0.12 AND char_freq_! <= AND char_freq_$<=0.172 AND word_freq_george> 0.0: 0 (Instances: 553 and Incorrect: 0) Total 42 rules are formulated when training. V. Results a) Smapbase Results The dataset spambase was taken from UCI machine learning repository [13]. Spambase dataset contains 4601 instances and 58 attributes continuous attributes and 1 nominal class label. The spam classification has been implemented in Eclipse. Eclipse considered by many to be the best Java development tool available. Feature ranking and feature selection is done by using the methods such as Chi-square, Information gain, Gain ratio, Relief, OneR, Correlation as a preprocessing step so as to select feature subset for building the learning model. Classification algorithms are from decision tree family, viz, Random Forest and Partial Decision Trees. Random forest is an effective tool in prediction. Because of the law of large numbers they do not over fit. Random inputs and random features produce good results in classification-less so in regression. For the larger data sets, it seems that significantly lower error rates are possible [14]. Feature space can be reduced by the magnitude of 10 while achieving similar classification results. For example, it takes about 2,000 features to achieve similar accuracies as those obtained with 149 PART features [15]. As a part of our implementation, we have divided the dataset into two parts. 80% of the dataset is used for training purpose and 20% for the testing purpose. After preprocessing step top 87%, 77% and 70% features are considered while building training model and testing because there is a significant performance improvement. Prediction accuracy, correctly classified instances, incorrectly classified instances, confusion matrix and time complexity are used as performance measures of the system. More than 99% prediction accuracy is achieved by Random forest with all the seven feature selection methods in consideration; whereas 97% prediction accuracy is achieved by PART with almost all the seven feature selection methods while training the model. Training and testing results, when 100% features have considered are given in Table 2. Table 2 : Results of 100% feature selection Classifier Training Testing Time Random Forest PART

8 Both training results and testing results on spambase dataset after feature ranking and subset selection are shown in the Table 3 and Table 4. Table 3 : Training Results RF Acc Time Table 4 : Testing Results Part Acc Time 87% Chi Infogain Gainratio Relief SU OneR Corr % Chi Infogain Gainratio Relief SU OneR Corr % Chi Infogain Gainratio Relief SU OneR Corr RF Acc Part Acc 87% Chi Infogain Gainratio Relief SU OneR % RF Acc Part Acc Corr Chi Infogain Gainratio Relief SU OneR Corr % Chi Infogain Gainratio Relief SU OneR Corr From the results above, it can be observed that for Random Forest, after using 87% of the feature set extracted the training accuracy is (96.012%) whereas the computation time reduced by % (from 9466ms to 4584ms). This shows that the remaining 13% features were not contributing towards the classification. Also, it can be observed that for Part, after using 87%, 77% of the feature set extracted the training accuracy is increased. There is a significant improvement in 87% feature selection by 1% and computation time is reduced by % (from ms to 5961ms). This shows that not only the remaining 30% features were redundant but also they were misleading the classification. VI. Enron Results More than 96% prediction accuracy is achieved by Random forest with all the seven feature selection methods in consideration; whereas more than 95% prediction accuracy is achieved by PART with almost all the seven feature selection methods while training the model. Training and testing results, when 100% features have considered are given in Table 5. Table 5 : Results of 100% feature selection Classifier Training Testing Time Random Forest PART Both training and testing results after feature ranking and subset selection are shown in the Table 6 and Table 7. Table 6 : Training Results RF Acc Time Part Acc Time 87% Chi Infogain Gainratio Relief SU OneR Corr

9 56 2 Table 7 : Testing Results From the results above, it can be observed that for Random Forest, after using 87% of the feature set extracted the training accuracy is (96.012%) whereas the computation time reduced by % (from 9466ms to 4584ms). This shows that the remaining 13% features were not contributing towards the classification. Also, it can be observed that for Part, after using 87%, 77% of the feature set extracted the training accuracy is increased. There is a significant improvement in 87% feature selection by 1% and computation time is reduced by % (from ms to 5961ms). This shows that not only the remaining 30% features were redundant but also they were misleading the classification. VI. Conclusion In this paper we have studied previous approaches of spam detection using machine learning methodologies. We have compared and evaluated the approaches based on the factors such as dataset used; features extracted, ranked and selected; feature selection algorithms used and the results received in terms of accuracy (precision, recall and error rate) and performance (time required). The datasets available for spam detection are large in number and for such larger datasets Random Forest and Part tend to produce better results with lower error rates and higher precision. So, we used these two classifiers to classify spam detection. For spambase dataset, we acquired the best percentage RF Acc Part Acc 87% Chi Infogain Gainratio Relief SU OneR Corr G-mail Dataset Test Results Further, we have tested our Enron model on the dataset created by using s we have received in our Gmail accounts during the period of last 3 months. The results are shown in the Table 8. In this, experiment we test dataset is completely non-overlapping with the training set allowing us to truly evaluate the performance of our system. Table 8 : Personal Dataset Testing Results Classifier Testing Accuracy Random Forest 96 PART accuracy of % with Random Forest which is 9% better than previous spambase approaches and % with Part. For enron dataset, we acquired the best percentage accuracy of % with Random Forest and % with Part. Enron dataset is used by [21] in an unsupervised spam learning and detection scheme. The feature selection algorithms used also contributed to achieve better accuracy with lower time complexity due to dimensionality reduction. For Random Forest, after using 70% of the feature set extracted, for spambase data set, the training accuracy remained the same (99.918%) whereas the computation time reduced by 20% (from 1540ms to 1276ms), whereas for PART, the training accuracy is increased by 1.521% and computation time is reduced by 52% (from 4938 ms to 2409ms). References Références Referencias 1. A Study of Spam classification using Feature Selection package, R.Parimala, Dr. R. Nallaswamy, National Institute of Technology, Global Journal of Computer Science and Technology, Volume 11 Issue 7 Version 1.0 May Comparative Study on Spam Classifier using Data Mining Techniques, R. Kishore Kumar, G. Poonkuzhali, P. Sudhakar, Member, IAENG, Proceedings of the International Multiconference of Engineers and Computer Scientists 2012 Vol I, IMECS 2012, March 14-16, Hong Kong. 3. Machine Learning Methods for Spam Classification, W.A. Awad and S.M. ELseuofi, International Journal of Computer Applications ( ) Volume 16 No.1, February Spam Filtering using Supervised Machine Learning Techniques, V.Christina, S.Karpagavalli, G.Suganya, (IJCSE) International Journal on Computer Science and EngineeringVol. 02, No. 09, 2010, Classification Using Data Reduction Method, Rafiqul Islam and Yang Xiang, member IEEE, School of Information Technology Deakin University, Burwood 3125, Victoria, Australia. 6. Spam Classification based on Supervised Learning using Machine Learning Techniques, Ms.D Karthika Renuka, Dr.T.Hamsapriya, Mr.M.Raja Chakkaravarthi, Ms. P. Lakshmi Surya, /11/$ IEEE. 7. An Empirical Performance Comparison of Machine Learning Methods for Spam Categorization, Chih-Chin Lai, Ming-Chi Tsai, Proceedings of the Fourth International Conference on Hybrid Intelligent Systems (HIS 04) /04 $ IEEE. 8. Introductory Statistics: Concepts, Models, and Applications, David W. Stockburger. 9. Feature Subset Selection: A Correlation Based Filter Approach, Hall, M. A., Smith, L. A., 1997,

10 International Conference on Neural Information Processing and Intelligent Information Systems, Springer, p A practical approach to feature selection, K. Kira and L. A. Rendell, Proceedings of the Ninth International Conference, Very simple classification rules perform well on most commonly used datasets, Holte, R.C.(1993) Machine Learning, Vol. 11, Induction of decision trees, J.R. Quinlan, Machine Learning 1, , UCI repository of Machine learning Databases, Department of Information and Computer Science, University of California, Irvine, CA, Hettich, S., Blake, C. L., and Merz, C. J., Random Forests, Leo Breiman, Statistics Department University of California Berkeley, CA 94720, January Exploiting Partial Decision Trees for Feature Subset Selection in Categorization, Helmut Berger, Dieter Merkl, Michael Dittenbach, SAC 06 April 2327, 2006, Dijon, France Copyright 2006 ACM /06/ C4.5: Programs for Machine Learning, J. R. Quinlan, Morgan Kaufmann Publishers Inc., Fast effective rule induction, W. W. Cohen, In Proc. of the Int l Conf. on Machine Learning, pages Morgan Kaufmann, Toward optimal feature selection using Ranking methods and classification Algorithms, Jasmina Novaković, PericaStrbac, DusanBulatović, March SpamAssassin, The enron spam dataset ion/data/enron-spa 21. A Case for Unsupervised-Learning-based Spam Filtering, Feng Qian, Abhinav Pathak, Y. Charlie Hu, Z. Morley Mao, Yinglian Xie. 57

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