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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(4): 258-263 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.517

Filter Based Feature Selection Methods for Prediction of Risks in Hepatitis Disease

Pinar Yildirim
Abstract—Recently, large amount of data is widely available in information systems and data mining has attracted a big attention to researchers to turn such data into useful knowledge. This implies the existence of low quality, unreliable, redundant and noisy data which negatively affect the process of observing knowledge and useful pattern. Therefore, researchers need relevant data from huge records using feature selection methods. Feature selection is the process of identifying the most relevant attributes and removing the redundant and irrelevant attributes. In this study, a comparison between filter based feature selection methods based on a well-known dataset (i.e., hepatitis dataset) was carried out and four classification algorithms were used to evaluate the performance of the algorithms. Among the algorithms, Naïve Bayes and Decision Table classifiers have higher accuracy rates on the hepatitis dataset than the others after the application of feature selection methods. The study revealed that feature selection methods are capable to improve the performance of learning algorithms. However, no single filter based feature selection method is the best. Overall, Consistency Subset, Info Gain Attribute Eval, One-R Attribute Eval and Relief Attribute Eval methods performed better results than the others.

Index Terms—Feature selection, hepatitis, J48, naïve bayes, IBK, decision table.

Pinar Yildirim is with the Okan University, Istanbul, Turkey (e-mail: pinar.yildirim@ okan.edu.tr).


Cite: Pinar Yildirim, "Filter Based Feature Selection Methods for Prediction of Risks in Hepatitis Disease," International Journal of Machine Learning and Computing vol. 5, no. 4, pp. 258-263, 2015.

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