Email spam classification using neighbor probability based Naïve Bayes algorithm
- Resource Type
- Conference
- Authors
- Anitha, P. U.; Rao, C. V. Guru; Babu, Suresh
- Source
- 2017 7th International Conference on Communication Systems and Network Technologies (CSNT) Communication Systems and Network Technologies (CSNT), 2017 7th International Conference on. :350-355 Nov, 2017
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Electronic mail
Postal services
Support vector machines
Training
Classification algorithms
Detectors
Mathematical model
Ham
Modified Naïve Bayes
Naive Bayes
Spam
Supporting Vector Machine
- Language
Email spam is a kind of electronic spam, which tends to be a more difficult problem nowadays among all internet challenges. Spam mails are mostly sent in commercial purpose, some of them may contain malware links that lead to phishing websites. The aim of this study is to classify into ham and spam emails with an optimized and well efficient classification technique. Ham holds emails that are legitimate or legally valid message can get accepted by users. Spam emails are unwanted emails that a user doesn't want and to get rid of it. This study emphasizes on the improvement in classifying all mails into these two groups with minimal requirement of training and with an accuracy of hundred percent. Here in this study, Modified Naïve Bayes (MNB) classifier ensured the requirements with very low percentage of training and produces accurate results than existing Naïve Bayes (NB) or Supporting Vector Machine (SVM) classifier.