Welcome to the InfoSci Platform
Reference Hub10
Spam Mail Filtering Using Data Mining Approach: A Comparative Performance Analysis

Spam Mail Filtering Using Data Mining Approach: A Comparative Performance Analysis

Ajay Kumar Gupta
Copyright: © 2020 |Pages: 30
ISBN13: 9781799824916|ISBN10: 1799824918|ISBN13 Softcover: 9781799824923|EISBN13: 9781799824930
DOI: 10.4018/978-1-7998-2491-6.ch015
Cite Chapter Cite Chapter

MLA

Gupta, Ajay Kumar. "Spam Mail Filtering Using Data Mining Approach: A Comparative Performance Analysis." Handling Priority Inversion in Time-Constrained Distributed Databases, edited by Udai Shanker and Sarvesh Pandey, IGI Global, 2020, pp. 253-282. https://doi.org/10.4018/978-1-7998-2491-6.ch015

APA

Gupta, A. K. (2020). Spam Mail Filtering Using Data Mining Approach: A Comparative Performance Analysis. In U. Shanker & S. Pandey (Eds.), Handling Priority Inversion in Time-Constrained Distributed Databases (pp. 253-282). IGI Global. https://doi.org/10.4018/978-1-7998-2491-6.ch015

Chicago

Gupta, Ajay Kumar. "Spam Mail Filtering Using Data Mining Approach: A Comparative Performance Analysis." In Handling Priority Inversion in Time-Constrained Distributed Databases, edited by Udai Shanker and Sarvesh Pandey, 253-282. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2491-6.ch015

Export Reference

Mendeley
Favorite

Abstract

This chapter presents an overview of spam email as a serious problem in our internet world and creates a spam filter that reduces the previous weaknesses and provides better identification accuracy with less complexity. Since J48 decision tree is a widely used classification technique due to its simple structure, higher classification accuracy, and lower time complexity, it is used as a spam mail classifier here. Now, with lower complexity, it becomes difficult to get higher accuracy in the case of large number of records. In order to overcome this problem, particle swarm optimization is used here to optimize the spam base dataset, thus optimizing the decision tree model as well as reducing the time complexity. Once the records have been standardized, the decision tree is again used to check the accuracy of the classification. The chapter presents a study on various spam-related issues, various filters used, related work, and potential spam-filtering scope.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.