Robust rule-based prediction

Li, Jiuyong (2006) Robust rule-based prediction. IEEE Transactions on Knowledge and Data Engineering, 18 (8). pp. 1043-1054. ISSN 1041-4347

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Abstract

This paper studies a problem of robust rule-based classification, i.e. making predictions in the presence of missing values in data. This study differs from other missing value handling research in that it does not handle missing values but builds a rule based classification model to tolerate missing values. Based on a commonly used rule-based classification model, we characterise the robustness of a hierarchy of rule sets, k-optimal rule sets with the decreasing size corresponds to the decreasing robustness. We build classifiers based on k-optimal rule sets and show experimentally that they are more robust than some benchmark rule-based classifiers, such as C4.5rules and CBA.We also show that the proposed approach is better than two well known missing value handling methods for missing values in test data.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Date Deposited: 11 Oct 2007 00:57
Last Modified: 02 Jul 2013 22:42
Uncontrolled Keywords: data mining, rule, classification, robustness
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: https://doi.org/10.1109/TKDE.2006.129
URI: http://eprints.usq.edu.au/id/eprint/2088

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