A Comparison of Different Classification Techniques for Bank Direct Marketing
K. Wisaeng
K. Wisaeng, Mahasarakham Business School (MBS), Mahasarakham University, Kantharawichai, khamriang, Maha sarakham, Thailand.
Manuscript received on August 05, 2013. | Revised Manuscript received on August 28, 2013. | Manuscript published on September 05, 2013. | PP: 116-119 | Volume-3, Issue-4, September 2013. | Retrieval Number: D1789093413/2013©BEIESP
Open Access | Ethics and Policies | Cite
© 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 a data mining techniques used to predict group membership for data instance. In this paper, we present the comparison of different classification techniques in open source data mining software which consists of a decision tree methods and machine learning for a set of bank direct marketing dataset. All decision tree methods tested are J48-graft and LAD tree while machine learning tested are radial basis function network and support vector machine. The experiment results show are a bout classification sensitivity, specificity, accuracy, mean absolute error and root mean squared error. The results on bank direct marketing data also the efficiency of machine learning methods by using support vector machine is better than that of all algorithms.
Keywords: Data mining, bank direct marketing, J48-graft, LAD tree, radial basis function network, support vector machine.