A Back Propagation and Gini’s Index Based Approach for Mobile Spam Classification
Shrutika Bhargava, Dr. Sachin Patel
Mobile Spam Classification, Gini’s Index, Neural Networks, Back Propagation, Training Iterations, Classification Accuracy.
With increased internet usage, one of the most prevalent problems faced is constant spamming. While web applications and mailing services are heavily spammed, the upsurge of handheld mobile devices has led to an outburst of heavy mobile spamming. The matter is more severe in mobile devices due to lesser sophisticated filtering mechanisms in built in mobile operating systems. Spam detection is challenging due to the need for semantic analysis of the mobile spam messages, which generally tend to have overlapping polarities. In this paper, a mobile spam classification technique is developed based on Gini’s index and Back-propagation in machine learning. The approach uses the Gini’s splitting criteria for the data sets and backpropagation based neural network as the machine learning classifier. The probabilistic classifier is well suited for datasets of texts messages with overlapping boundaries. The evaluation of the proposed system is based on the accuracy of classification and number of iterations. The results obtained in the proposed work are compared with existing techniques and it is shown that the proposed technique outperforms them in terms of accuracy of classification.
Article Details
Unique Paper ID: 158626

Publication Volume & Issue: Volume 9, Issue 10

Page(s): 877 - 882
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