Robust rule-based prediction

Article

Li, Jiuyong. 2006. "Robust rule-based prediction." IEEE Transactions on Knowledge and Data Engineering. 18 (8), pp. 1043-1054. https://doi.org/10.1109/TKDE.2006.129
Article Title

Robust rule-based prediction

ERA Journal ID17876
Article CategoryArticle
Authors
AuthorLi, Jiuyong
Journal TitleIEEE Transactions on Knowledge and Data Engineering
Journal Citation18 (8), pp. 1043-1054
Number of Pages12
Year2006
PublisherIEEE (Institute of Electrical and Electronics Engineers)
ISSN1041-4347
1558-2191
Digital Object Identifier (DOI)https://doi.org/10.1109/TKDE.2006.129
Web Address (URL)https://ieeexplore.ieee.org/document/1644728
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.

Keywordsdata mining, rule, classification, robustness
ANZSRC Field of Research 2020469999. Other information and computing sciences not elsewhere classified
Public Notes

© 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.

Byline AffiliationsDepartment of Mathematics and Computing
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