Spam Mail Detection through Data Mining – A Comparative Performance Analysis

Abstract

As web is expanding day by day and people generally rely on web for communication so e-mails are the fastest way to send information from one place to another. Now a day's all the transactions all the communication whether general or of business taking place through e-mails. E-mail is an effective tool for communication as it saves a lot of time and cost. But emails are also affected by attacks which include Spam Mails. Spam is the use of electronic messaging systems to send bulk data. Spam is flooding the Internet with many copies of the same message, in an attempt to force the message on people who would not otherwise choose to receive it. In this study, we analyze various data mining approach to spam dataset in order to find out the best classifier for email classification. In this paper we analyze the performance of various classifiers with feature selection algorithm and without feature selection algorithm. Initially we experiment with the entire dataset without selecting the features and apply classifiers one by one and check the results. Then we apply Best-First feature selection algorithm in order to select the desired features and then apply various classifiers for classification. In this study it has been found that results are improved in terms of accuracy when we embed feature selection process in the experiment. Finally we found Random Tree as best classifier for spam mail classification with accuracy = 99.72%. Still none of the algorithm achieves 100% accuracy in classifying spam emails but Random Tree is very nearby to that.

Comparative Analysis of Classification Algorithms for Email Spam Detection

“…Classification algorithms whose performances have been so far compared include Naï ve Bayes [1], [12]- [17], other algorithms compared include C-PLS, ANN, C-RT, CS-CRT, CS-MC4, CS-SVC, Continouns PLS-DA, PLS-LDA, LDA [1], Bayesnet [4], [12], [13], Multilayer perceptron [1], [15], SVM [1], [4], [12]- [14], [16], [17]. Table 1 shows the summary of algorithms used in previous comparative research.…”

“…Finally, The Rnd tree classifier for email spam is identified as the best based on the error rate, precision and recall. The researchers employed the use of a combination of some performance metrics including Correctly Classified Instances, Kappa Statistics, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error [12]. Other performance metrics used are TP Rate, FP Rate, Precision, Recall, F-Measure and ROC [4], [13].…”

An Improved Mechanism Using J48 Classifiers for Spam Filtering

“…The separating hyper plane is the hyper plane that maximizes the distance between the two hyper planes. Larger the margin or distance better the generalization error of the classifier [20].…”

K-MLP Based Classifier for Discernment of Gratuitous Mails using N-Gram Filtration

“…Megha Rathi and Vikas Pareek(2013) performed an analysis on spam email detection through Data Mining by performing analysis on classifiers by selecting and without selecting the features [6]. algorithms give better results without pre-processing among which Naï ve Bayes algorithm is highly accurate than other algorithms [7].…”