Analysis of Machine Learning Methods Using Spam Filtering

Naive Bayesian ◽

Machine Learning Methods ◽

Naïve Bayesian

The paper considers methods of the naive Bayesian classifier. Experiments that show independence between traits are described. Describes the naive Bayesian classifier used to filter spam in messages. The aim of the study is to determine the best method to solve the problem of spam in messages. The paper considers three different variations of the naive Bayesian classifier. The results of experiments and research are given.

Comparison of a SOM based sequence analysis system and naive Bayesian classifier for spam filtering

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. ◽

10.1109/ijcnn.2005.1556308 ◽

2006 ◽

Author(s):

Xiao Luo ◽

N. Zincir-Heywood

Keyword(s):

Sequence Analysis ◽

Bayesian Classifier ◽

Spam Filtering ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Analysis System

E-Mail Spam Classification using Naive Bayesian Classifier

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36153 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 5209-5214

Author(s):

Mr. B. Krishna

Keyword(s):

Bayesian Classifier ◽

Spam Filtering ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Filtering Technique ◽

E Mail ◽

Feature Extraction Technique

— E-mail spam is the very recent problem for every individual. The e-mail spam is nothing it’s an advertisement of any company/product or any kind of virus which is receiving by the email client mailbox without any notification. To solve this problem the different spam filtering technique is used. The spam filtering techniques are used to protect our mailbox for spam mails. In this project, we are using the Naïve Bayesian Classifier for spam classification. The Naïve Bayesian Classifier is very simple and efficient method for spam classification. Here we are using the Lingspam dataset for classification of spam and non-spam mails. The feature extraction technique is used to extract the feature. The result is to increase the accuracy of the system.

IMPLEMENTASI NAÏVE BAYESIAN CLASSIFIER UNTUK KASUS FILTERING SMS SPAM

Jurnal Informatika ◽

10.21460/inf.2013.92.317 ◽

2014 ◽

Vol 9 (2) ◽

Author(s):

Gilang Jalu Selo W.T. ◽

Budi Susanto

Keyword(s):

Classification System ◽

Bayesian Classifier ◽

Spam Filtering ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Text Preprocessing

In 2011, the circulation of SMS spam in Indonesia was rampant. The SMS can contain promotion of a product which is often unsolicited by the recipient or fraud. This is an overlooked issue in Indonesia. But spam has been a very common topic in other countries. To resolve these problems, we need a system that can recognize SMS spam so the SMS can be diverted or marked prior to the user. In this research, we built a system that implementing the Naive Bayesian classifier for classifying SMS spam, so the user can recognize the SMS spam. The result of this research, the system built is able to classify a SMS into categories spam and not spam. Naïve Bayesian classifier can be implemented effectively in the case of SMS spam filtering. The proper use of text preprocessing can improve the performance of this classification system.