A Content-Based Spam E-Mail Filtering Approach Using Multilayer Percepton Neural Networks

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2016 by IJETT Journal
Volume-41 Number-1
Year of Publication : 2016
Authors : A.Sesha Rao, P.S.Avadhani, Nandita Bhanja Chaudhuri
DOI :  10.14445/22315381/IJETT-V41P210


A.Sesha Rao, P.S.Avadhani, Nandita Bhanja Chaudhuri"A Content-Based Spam E-Mail Filtering Approach Using Multilayer Percepton Neural Networks", International Journal of Engineering Trends and Technology (IJETT), V41(1),44-55 November 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Nowadays increased spam e-mails are causing inconvenience to internet users and organizations and are considered as a serious wastage of resources, time, memory, space and efforts. Therefore, it is crucial to have an automatic e-mail classification system for the identification of spam e-mails. Spam mails need to be classified and separated from ham (non-spam) mails as they are the source of financial loss and annoyance for the recipients. The spam e-mail classifier performance can be greatly enhanced with the use of Artificial Neural Network classification. It has capability of learning huge amount of data with high dimensionality in a better way. In this paper, Multilayer Perceptron and Back Propagation Training algorithm is explored where ‘generalized delta’ rule is used for weight adjustments for hidden layers. The Perceptron uses Back Propagation Learning model for calculating its gradient. For fast convergence the learning rate ? is changed for every iteration which is proportional to the negative gradient of the instantaneous error with respect to ?. To avoid the local minima problem the weights are initialized to small random numbers which are uniformly distributed in the range [ -?/ , + ?/ ], where Ni is the number of inputs, and ? takes value in (1, 3). In this paper, four Multilayer Perceptron (MLP) Network models are constructed. For testing our model bench mark data drawn from UCI, Machine learning Repository is employed for training the neural network. The results of our MLP model are reasonable in terms of TP rate, FP Rate, Accuracy, Precision, Recall, F-measure, MCC, ROC Area, PRC Area.


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back propagation, delta rule, F-measure, hidden layers, learning rate, local minima, MCC, perceptron, precision, PRC Area, recall, ROC.