A Comparative Study of Classification Algorithms on Aliphatic Carboxylic Acids Data Set using WEKA
Kavitha C.R1, Mahalekshmi T2

1Kavitha C.R, Research Scholar, R&D, Bharathiar University, Coimbatore, India.
2Dr. Mahalekshmi T, Principal, Sree Narayana Institute of Technology, Kollam, India.
Manuscript received on May 15, 2015. | Revised Manuscript received on May 20, 2015. | Manuscript published on May 25, 2015. | PP:25-29 | Volume-3 Issue-7, May 2015. | Retrieval Number: G0976053715

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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Classification is the process of arranging a number of items into groups in such a manner that the members of the group have one or more characteristics in common. In this research paper, we present a comparative study of five different classification algorithms using WEKA, a data mining tool. This article gives an overview about the classification algorithms such as ZeroR, Naïve Bayes, J48, IBK and SMO. The dataset used for conducting the experiment is the toxicity dataset of aliphatic carboxylic acids. The main aim of this paper is to make a comparison of different classification algorithms and to find out the best algorithm out of the five chosen algorithm which gives the most accurate result.
Keywords: Classification, ZeroR, Naïve Bayes, J48, IBK, SMO, WEKA