Using association rules to make rule-based classifiers robust

Hu, Hong and Li, Jiuyong (2005) Using association rules to make rule-based classifiers robust. In: ADC 2005: 16th Australasian Database Conference, 31 Jan-3 Feb 2005, Newcastle, Australia.

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Abstract

Rule-based classification systems have been widely used in real world applications because of the easy interpretability of rules. Many traditional rule-based classifiers prefer small rule sets to large rule sets, but small classifiers are sensitive to the missing values in unseen test data. In this paper, we present a larger classifier that is less sensitive to the missing values in unseen test data. We experimentally show that it is more accurate than some benchmark classifies when unseen test data have missing values.


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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 publsiher. Copyright 2005, Australian Computer Society, Inc. This paper appeared at the 16th Australasian Database Conference, University of Newcastle, Newcastle, Australia. Conferences in Research and Practice in Information Technology, Vol. 39. H.E. Williams and G. Dobbie, Eds. Reproduction for academic,not-for profit purposes permitted provided this text is included.
Depositing User: Dr Zhongwei Zhang
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 24 Jun 2009 12:05
Last Modified: 02 Jul 2013 23:07
Uncontrolled Keywords: data mining; association rule; classification; robustness
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
URI: http://eprints.usq.edu.au/id/eprint/4470

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