Comparing the knowledge quality in rough classifier and decision tree classifier

This paper presents a comparative study of two rule based classifier; rough set (R
c
) and decision tree (DT
c
). Both techniques apply different approach to perform classification but produce same structure of output with comparable result. Theoretically, different classifiers will generate different sets of rules via knowledge even though they are implemented to the same classification problem. Hence, the aim of this paper is to investigate the quality of knowledge produced by R
c
and DT
c
when similar problems are presented to them. In this case, four important performance metrics are used as comparison, the accuracy of classification, rules quantity, rules length and rules coverage. Five dataset from UCI Machine Learning are chosen and then mined using R
c
toolkit namely ROSETTA while C4.5 algorithm in WEKA application is chosen as DT
c
rule generator. The experimental result shows that R
c
and DT
c
own capability to generate quality knowledge since most of the results are comparable. R
c
outperform as an accurate classifier, produce shorter and simpler rule with higher coverage. Meanwhile, DT
c
obviously generates fewer numbers of rules with significant difference.