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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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.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|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.|
|Uncontrolled Keywords:||data mining; association rule; classification; robustness|
|Subjects:||280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280201 Expert Systems|
|Depositing User:||Dr Zhongwei Zhang|
|Date Deposited:||24 Jun 2009 12:05|
|Last Modified:||02 Jul 2013 23:07|
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