The metric dilemma: Competence-conscious associative classification

Adriano Veloso, Mohammed Zaki, Wagner Meira, Marcos Gonçalves

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

1 Citation (Scopus)

Abstract

The classification performance of an associative classifier is strongly dependent on the statistic measure or metric that is used to quantify the strength of the association between features and classes (i.e., confidence, correlation etc.). Previous studies have shown that classifiers produced by different metrics may provide conflicting predictions, and that the best metric to use is data-dependent and rarely known while designing the classifier. This uncertainty concerning the optimal match between metrics and problems is a dilemma, and prevents associative classifiers to achieve their maximal performance. This dilemma is the focus of this paper. A possible solution to this dilemma is to learn the competence, expertise, or assertiveness of metrics. The basic idea is that each metric has a specific sub-domain for which it is most competent (i.e., it consistently produces more accurate classifiers than the ones produced by other metrics). Particularly, we investigate stacking-based meta-learning methods, which use the training data to find the domain of competence of each metric. The meta-classifier describes the domains of competence (or areas of expertise) of each metric, enabling a more sensible use of these metrics so that competence-conscious classifiers can be produced (i.e., a metric is only used to produce classifiers for test instances that belong to its domain of competence). We conducted a systematic evaluation, using different datasets and evaluation measures, of classifiers produced by different metrics. The result is that, while no metric is always superior than all others, the selection of appropriate metrics according to their competence/expertise (i.e., competence-conscious associative classifiers) seems very effective, showing gains that range from 7% to 26% when compared to the baselines (SVMs and an existing ensemble method).

Original languageEnglish
Title of host publicationSociety for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics
Pages913-924
Number of pages12
Volume2
Publication statusPublished - 31 Dec 2009
Externally publishedYes
Event9th SIAM International Conference on Data Mining 2009, SDM 2009 - Sparks, NV, United States
Duration: 30 Apr 20092 May 2009

Other

Other9th SIAM International Conference on Data Mining 2009, SDM 2009
CountryUnited States
CitySparks, NV
Period30/4/092/5/09

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ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software
  • Applied Mathematics

Cite this

Veloso, A., Zaki, M., Meira, W., & Gonçalves, M. (2009). The metric dilemma: Competence-conscious associative classification. In Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics (Vol. 2, pp. 913-924)