2000 ImprovingAnAssocRuleBasedClassifier

• (Liu et al., 2000) ⇒ Bing Liu, Yiming Ma, Ching Kian Wong. (2000). “Improving an Association Rule Based Classifier.” In: Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2000). doi:10.1007/3-540-45372-5

Subject Headings: Association Rule, Classifier.

Quotes

Abstract

Existing classification algorithms in machine learning mainly use heuristic search to find a subset of regularities in data for classification. In the past few years, extensive research was done in the database community on learning rules using exhaustive search under the name of association rule mining. Although the whole set of rules may not be used directly for accurate classification, effective classifiers have been built using the rules. This paper aims to improve such an exhaustive search based classification system CBA (Classification Based on Associations). The main strength of this system is that it is able to use the most accurate rules for classification. However, it also has weaknesses. This paper proposes two new techniques to deal with these weaknesses. This results in remarkably accurate classifiers. Experiments on a set of 34 benchmark datasets show that on average the new techniques reduce the error of CBA by 17% and is superior to CBA on 26 of the 34 datasets. They reduce the error of C4.5 by 19%, and improve performance on 29 datasets. Similar good results are also achieved against RIPPER, LB and a Naïve-Bayes classifier.,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2000 ImprovingAnAssocRuleBasedClassifier	Bing Liu Yiming Ma Ching Kian Wong			Improving an Association Rule Based Classifier			http://www.cs.uic.edu/~liub/publications/pkdd2000.ps	10.1007/3-540-45372-5