Fuzzy Improved Decision Tree Approach for Outlier Detection in SMS

Abstract

Spam is one of the serious problems faced by internet community globally. Spam Detection is a critical issue in business world. In this paper an intelligent three stage model is presented to perform the spam inclusive outlier identification. The SMS textual dataset is taken as input and than its filtration is done. After that this textual information is converted to the statistical information using fuzzy and assign the weights to dataset. The decision tree algorithm is than applied on this fuzzy weighed dataset to classify the dataset. This algorithm is defined to separate the spam and non spam data values. A comparison of existing Bayesian and proposed Fuzzy based decision tree approach is done. The results shows that the recognition rate is improved using the proposed approach. The work is implemented in weka integrated java environment.