Comparing the knowledge quality in rough classifier and decision tree classifier

Mohd Mohsin, Mohd Farhan and Abd Wahab, Mohd Helmy (2008) Comparing the knowledge quality in rough classifier and decision tree classifier. In: International Symposium on Information Technology 2008, Kuala Lumpur Convention Centre, Kuala Lumpur, Malaysia.

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Abstract

This paper presents a comparative study of two rule based classifier; rough set (RC) and decision tree (DTC). 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 RC and DTC 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 RC toolkit namely ROSETTA while C4.5 algorithm in WEKA application is chosen as DTC rule generator. The experimental result shows that RC and DTC own capability to generate quality knowledge since most of the results are comparable. RC outperform as an accurate classifier, produce shorter and simpler rule with higher coverage. Meanwhile, DTC obviously generates fewer numbers of rules with significant difference.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:biodiesel, aata mining concept, Universiti Tun Hussein Onn Malaysia,
Subjects:T Technology > T Technology (General)
T Technology > TS Manufactures
Divisions:Faculty of Electrical and Electronic Engineering > Department of Electronic Engineering
ID Code:177
Deposited By:Khairunnisa Ahmad
Deposited On:15 Apr 2010 12:13
Last Modified:29 Apr 2011 14:39

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