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Competence‐conscious Associative Classification by Adriano Veloso; Mohammed Zaki; Wagner Meira; Marcos Gonçalves is a Computer Science article available to read on EtoBox.

What is Competence‐conscious Associative Classification about?

## 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 metr

Who reads Competence‐conscious Associative Classification?

Author

Adriano Veloso; Mohammed Zaki; Wagner Meira; Marcos Gonçalves

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); Wiley Subscription Services; Wiley (ISSN 1932-1864)

Published

2009

Field

Computer Science (Physical Sciences)