Lazy associative classification

Adriano Veloso, Wagner Meira, Mohammed J. Zaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

94 Citations (Scopus)

Abstract

Decision tree classifiers perform a greedy search for rules by heuristically selecting the most promising features. Such greedy (local) search may discard important rules. Associative classifiers, on the other hand, perform a global search for rules satisfying some quality constraints (i.e., minimum support). This global search, however, may generate a large number of rules. Further, many of these rules may be useless during classification, and worst, important rules may never be mined. Lazy (non-eager) associative classification overcomes this problem by focusing on the features of the given test instance, increasing the chance of generating more rules that are useful for classifying the test instance. In this paper we assess the performance of lazy associative classification. First we demonstrate that an associative classifier performs no worse than the corresponding decision tree classifier. Also we demonstrate that lazy classifiers outperform the corresponding eager ones. Our claims are empirically confirmed by an extensive set of experimental results. We show that our proposed lazy associative classifier is responsible for an error rate reduction of approximately 10% when compared against its eager counterpart, and for a reduction of 20% when compared against a decision tree classifier. A simple caching mechanism makes lazy associative classification fast, and thus improvements in the execution time are also observed.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages645-654
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2006
Externally publishedYes
Event6th International Conference on Data Mining, ICDM 2006 - Hong Kong, China
Duration: 18 Dec 200622 Dec 2006

Other

Other6th International Conference on Data Mining, ICDM 2006
CountryChina
CityHong Kong
Period18/12/0622/12/06

Fingerprint

Classifiers
Decision trees

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Veloso, A., Meira, W., & Zaki, M. J. (2006). Lazy associative classification. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 645-654). [4053090] https://doi.org/10.1109/ICDM.2006.96

Lazy associative classification. / Veloso, Adriano; Meira, Wagner; Zaki, Mohammed J.

Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. p. 645-654 4053090.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Veloso, A, Meira, W & Zaki, MJ 2006, Lazy associative classification. in Proceedings - IEEE International Conference on Data Mining, ICDM., 4053090, pp. 645-654, 6th International Conference on Data Mining, ICDM 2006, Hong Kong, China, 18/12/06. https://doi.org/10.1109/ICDM.2006.96
Veloso A, Meira W, Zaki MJ. Lazy associative classification. In Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. p. 645-654. 4053090 https://doi.org/10.1109/ICDM.2006.96
Veloso, Adriano ; Meira, Wagner ; Zaki, Mohammed J. / Lazy associative classification. Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. pp. 645-654
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