Using association rules to make rule-based classifiers robust
Paper
Paper/Presentation Title | Using association rules to make rule-based classifiers robust |
---|---|
Presentation Type | Paper |
Authors | Hu, Hong (Author) and Li, Jiuyong (Author) |
Editors | Williams, Hugh E. and Dobbie, Gillian |
Journal or Proceedings Title | Conferences in Research and Practice in Information Technology |
Journal Citation | 39, pp. 47-54 |
Number of Pages | 8 |
Year | 2005 |
Place of Publication | Sydney, Australia |
ISBN | 192068221X |
Web Address (URL) of Paper | http://delivery.acm.org/10.1145/1090000/1082228/p47-hu.pdf?ip=139.86.2.14&acc=PUBLIC&CFID=51667743&CFTOKEN=55354070&__acm__=1320123430_304fc64bf73ff674f86216b8cccf7985 |
Conference/Event | ADC 2005: 16th Australasian Database Conference |
Event Details | ADC 2005: 16th Australasian Database Conference Event Date 31 Jan 2005 to end of 03 Feb 2005 Event Location Newcastle, Australia |
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. |
Keywords | data mining; association rule; classification; robustness |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
469999. Other information and computing sciences not elsewhere classified | |
461399. Theory of computation not elsewhere classified | |
Public Notes | 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. |
Byline Affiliations | Department of Mathematics and Computing |
https://research.usq.edu.au/item/9ywqy/using-association-rules-to-make-rule-based-classifiers-robust
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