Classification using multiple and negative target rules

Li, Jiuyong and Jones, Jason (2006) Classification using multiple and negative target rules. In: 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, 9-11 Oct 2006, Bournemouth, UK.

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Abstract

[Abstract]: Rules are a type of human-understandable knowledge, and rule-based methods are very popular in building decision support systems. However, most current rule based classification systems build small classifiers where no rules account for exceptional instances and a default prediction plays a major role in the prediction. In this paper, we discuss two schemes to build rule based classifiers using multiple and negative target rules. In such schemes, negative rules pick up exceptional instances and multiple rules provide alternative predictions. The default prediction is removed and hence all predictions relate to rules providing explanations for the predictions. One risk for building a large rule based classifier is that it may overfit training data and results in low predictive accuracy. We show experimentally that one classifier is more accurate than a benchmark rule based classifier, C4.5rules


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Deposited in accordance with the copyright policy of the publisher. Copyright 2006 Springer. This is the authors' version of the work. It is posted here with permission of the publisher for your personal use. No further distribution is permitted. The item is also available in Lecture Notes in Artificial Intelligence v. 4251 at http://www.springerlink.com
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 rule; negative rule; multiple rule
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
URI: http://eprints.usq.edu.au/id/eprint/2098

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