Classification using multiple and negative target rules

Paper

Li, Jiuyong and Jones, Jason. 2006. "Classification using multiple and negative target rules." Gabrys, Bogdan, Howlett, Robert J. and Jain, Lakhmi C. (ed.) 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2006). Bournemouth, United Kingdom 09 - 11 Oct 2006 Germany. https://doi.org/10.1007/11892960_26
Paper/Presentation Title

Classification using multiple and negative target rules

Presentation TypePaper
AuthorsLi, Jiuyong (Author) and Jones, Jason (Author)
EditorsGabrys, Bogdan, Howlett, Robert J. and Jain, Lakhmi C.
Journal or Proceedings TitleLecture Notes in Artificial Intelligence (Book series)
Journal Citation4251, pp. 212-219
Number of Pages8
Year2006
Place of PublicationGermany
ISBN9783540465355
Digital Object Identifier (DOI)https://doi.org/10.1007/11892960_26
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/11892960_26
Conference/Event10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)
Event Details
10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2006)
Parent
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems
Delivery
In person
Event Date
09 to end of 11 Oct 2006
Event Location
Bournemouth, United Kingdom
Abstract

Rules are a type of human-understandable knowledge, and rule-based methods are very popular in building decision support systems. However, most current rule based classification systems build small classifiers where no rules account for exceptional instances and a default prediction plays a major role in the prediction. In this paper, we discuss two schemes to build rule based classifiers using multiple and negative target rules. In such schemes, negative rules pick up exceptional instances and multiple rules provide alternative predictions. The default prediction is removed and hence all predictions relate to rules providing explanations for the predictions. One risk for building a large rule based classifier is that it may overfit training data and results in low predictive accuracy. We show experimentally that one classifier is more accurate than a benchmark rule based classifier, C4.5rules

Keywordsclassification; association rule; negative rule; multiple rule
ANZSRC Field of Research 2020469999. Other information and computing sciences not elsewhere classified
Public Notes

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Byline AffiliationsDepartment of Mathematics and Computing
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