JOURNAL ARTICLE

Feature Selection based Classification of Spams Using Fuzzy Support Vector Machine

Lovely BansalNirupama Tiwari

Year: 2020 Journal:   2020 International Conference on Smart Electronics and Communication (ICOSEC) Pages: 258-263

Abstract

E-mail is a safe way for the online form of communication as well as for transferring data or messages through the web. A standard increase in popularity, which gives rapid growth to unsolicited emails in bulk amount Numerous methods are available to filter the data which is to detect automatically as well as eliminate such untenable messages. There aoe a large number of techniques for email spam filtering such as clustering techniques, learning-based technique, knowledge-based technique, heuristic processes and many more. The research of spam filtering by using the feature selection through relief feature selection technique and then classifies data by using a fuzzy support vector machine to deal with the factors of uncertain type. The experimental outcome illustrates that given algorithms can improve the filtering rate parameters and spam filtering speedmore efficiently.

Keywords:
Computer science Feature selection Data mining Support vector machine Cluster analysis Filter (signal processing) Heuristic Machine learning Artificial intelligence Feature vector Fuzzy logic Selection (genetic algorithm) Popularity Feature (linguistics) The Internet World Wide Web

Metrics

5
Cited By
0.99
FWCI (Field Weighted Citation Impact)
16
Refs
0.78
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Spam and Phishing Detection
Physical Sciences →  Computer Science →  Information Systems
Network Security and Intrusion Detection
Physical Sciences →  Computer Science →  Computer Networks and Communications
Text and Document Classification Technologies
Physical Sciences →  Computer Science →  Artificial Intelligence

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