Using multiple and negative target rules to make classifiers more understandable

Li, Jiuyong and Jones, Jason (2006) Using multiple and negative target rules to make classifiers more understandable. Knowledge-Based Systems, 19 (6). pp. 438-444. ISSN 0950-7051

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Abstract

[Abstract]: One major goal for data mining is to understand data. Rule based methods are better than other methods in making mining results comprehensible. However, current rule based classifiers make use of a small number of rules and a default prediction to build a concise predictive model. This reduces the explanatory ability of the rule based classifier. In this paper, we propose to use multiple and negative target rules to improve explanatory ability of rule based classifiers. We show experimentally that this understandability is not at the cost of accuracy of rule based classifiers.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Authors' final corrected manuscript version. Deposited in accordance with the copyright policy of the publisher.
Depositing User: Dr Jiuyong (John) Li
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 11 Oct 2007 00:57
Last Modified: 02 Jul 2013 22:42
Uncontrolled Keywords: classification, association rules, negative and multiple rules
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080101 Adaptive Agents and Intelligent Robotics
Identification Number or DOI: doi: 10.1016/j.knosys.2006.03.003
URI: http://eprints.usq.edu.au/id/eprint/2089

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