Open AccessRotation Forest model modification within the email spam classification
Author(s) -
А.О. Шанін
Publication year - 2021
Publication title -
sistemi obrobki ìnformacìï
Language(s) - English
Resource type - Journals
eISSN - 2518-1696
pISSN - 1681-7710
DOI - 10.30748/soi.2021.164.12
Subject(s) - computer science , classifier (uml) , recall , random forest , bag of words model , spambot , machine learning , data mining , artificial intelligence , world wide web , the internet , spamming , philosophy , linguistics
Increased use of email in daily transactions for many businesses or general communication due to its cost-effectiveness has made emails vulnerable to attacks, including spam. Spam emails are unsolicited messages that are very similar to each other and sent to multiple recipients randomly. This study analyzes the Rotation Forest model and modifies it for spam classification problem. Also, the aim of this study is to create a better classifier. To improve classifier stability, the experiments were carried out on Enron spam, Ling spam, and SpamAssasin datasets and evaluated for accuracy, f-measure, precision, and recall.