NDLI: Comparing the knowledge quality in rough classifier and decision tree classifier

Content Provider	IEEE Xplore Digital Library
Author	Mohamad Farhan Mohamad Mohsin, Mohd Helmy Abd Wahab,
Copyright Year	2008
Description	Author affiliation: Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn, Malaysia (Mohd Helmy Abd Wahab,) \|\| College of Arts & Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia (Mohamad Farhan Mohamad Mohsin,)
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.
Starting Page	1
Ending Page	6
File Size	365111
Page Count	6
File Format	PDF
ISBN	9781424423279
DOI	10.1109/ITSIM.2008.4631700
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-26
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Satellite broadcasting Data models Classification algorithms Data mining Classification tree analysis Testing
Content Type	Text
Resource Type	Article

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