A Comparative Study with RapidMiner and WEKA Tools over some Classification Techniques for SMS Spam

International Research and Innovation Summit 2017

Mohd Foozy, Cik Feresa^1 ; Ahmad, Rabiah^2 ; Faizal Abdollah, M.A.^2 ; Wen, Chuah Chai^1

Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Batu Pahat, Malaysia^1

Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia^2

关键词: Classification technique;    Comparative studies;    Data-mining tools;    Decision stumps;    K-nearest neighbour algorithms;    Machine learning techniques;    Random forests;    UCI machine learning repository;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/226/1/012100/pdf
DOI  :  10.1088/1757-899X/226/1/012100

来源: IOP

【 摘 要 】

SMS Spamming is a serious attack that can manipulate the use of the SMS by spreading the advertisement in bulk. By sending the unwanted SMS that contain advertisement can make the users feeling disturb and this against the privacy of the mobile users. To overcome these issues, many studies have proposed to detect SMS Spam by using data mining tools. This paper will do a comparative study using five machine learning techniques such as Naïve Bayes, K-NN (K-Nearest Neighbour Algorithm), Decision Tree, Random Forest and Decision Stumps to observe the accuracy result between RapidMiner and WEKA for dataset SMS Spam UCI Machine Learning repository.