International Journal of Soft Computing
ISSN: OnlineISSN: Print 1816-9503
Abstract
Rough set theory introduced by Pawlak in 1982 has been applied successfully in all the fields. It creates a framework for handling imprecise and incomplete data in information systems. A Rough Set is a mathematical tool to deal with Uncertainty and vagueness of an information system. An information system can be presented as a Table with rows analogous to objects and columns analogous to attributes. Each row of the table contains values of particular attributes representing information about an object. Based on rough sets theory, this study proposes Modified Quickreduct algorithm and discusses the performance study of various reduct algorithms for constructing efficient rules. The experiments were carried out on data sets of UCI machine learning repository and the Human Immuno deficiency Virus(HIV) data set to analyze the performance study. Generally, in rule generation for taking decision from the information system, the reduct plays a vital role. The reduct algorithm that generates the least number of rules is considered an efficient one.
How to cite this article:
Thangavel, K. , A. Pethalakshmi and P. Jaganathan . A Comparative Analysis of Feature Selection Algorithms Based on Rough Set Theory.
DOI: https://doi.org/10.36478/ijscomp.2006.288.294
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2006.288.294