Evaluating Various Learning Techniques for Efficiency
Mehnaz Khan1, S.M.K. Quadri2
1Mehnaz Khan, Department of Computer Science, University of Kashmir, Srinagar, India.
2Dr. S.M.K. Quadri, Department of Computer Science, University of Kashmir, Srinagar, India.
Manuscript received on November 05, 2012. | Revised Manuscript received on December 12, 2012. | Manuscript published on December 30, 2012. | PP: 326-331 | Volume-2, Issue-2, December 2012.  | Retrieval Number: B0957112212 /2012©BEIESP

Open Access | Ethics and Policies | Cite
© The Authors. 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: Machine learning is a vast field and has a broad range of applications including natural language processing, medical diagnosis, search engines, speech recognition, game playing and a lot more. A number of machine learning algorithms have been developed for different applications. However no single machine learning algorithm can be used appropriately for all learning problems. It is not possible to create a general learner for all problems because there are varied types of real world datasets that cannot be handled by a single learner. In this paper we present an evaluation of various state-of-the-art machine learning algorithms using WEKA (Waikato Environment for Knowledge Analysis) for a real world learning problem- credit approval used in banks. First we provide a brief description about WEKA. After that we describe the learning problem and the dataset that we have used in our experiments. Later we explain the machine learning methods that we have evaluated. Finally we provide description about our experimental setup and procedure and discuss the conclusion and the result. 
Keywords: Credit approval, Machine learning, Test sets and Training Sets.