Analysis of Na?ve Bayes Algorithm for Email Spam Filtering across Multiple Datasets

International Research and Innovation Summit 2017

Rusland, Nurul Fitriah^1 ; Wahid, Norfaradilla^1 ; Kasim, Shahreen^1 ; Hafit, Hanayanti^1

Department of Web Technology, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia^1

关键词: Bayes algorithms;    E-mail spam;    F measure;    Filtering technique;    Multiple data sets;    Neutral network;    Random forests;    Weka tool;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/226/1/012091/pdf
DOI  :  10.1088/1757-899X/226/1/012091

来源: IOP

【 摘 要 】

E-mail spam continues to become a problem on the Internet. Spammed e-mail may contain many copies of the same message, commercial advertisement or other irrelevant posts like pornographic content. In previous research, different filtering techniques are used to detect these e-mails such as using Random Forest, Naïve Bayesian, Support Vector Machine (SVM) and Neutral Network. In this research, we test Naïve Bayes algorithm for e-mail spam filtering on two datasets and test its performance, i.e., Spam Data and SPAMBASE datasets [8]. The performance of the datasets is evaluated based on their accuracy, recall, precision and F-measure. Our research use WEKA tool for the evaluation of Naïve Bayes algorithm for e-mail spam filtering on both datasets. The result shows that the type of email and the number of instances of the dataset has an influence towards the performance of Naïve Bayes.