TY  - JOUR
T1  - A Comparative Analysis of Feature Selection Algorithms Based on Rough Set Theory
AU - , Thangavel, K. AU - , A. Pethalakshmi AU - , P. Jaganathan 
JO  - International Journal of Soft Computing
VL  - 1
IS  - 4
SP  - 288
EP  - 294
PY  - 2006
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2006.288.294
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2006.288.294
KW  - Data mining
KW  -rough set
KW  -feature selection
KW  -rule induction
AB  - 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.
ER  -