Competence–Conscious Associative Classification

Abstract

The classification performance of an associative classification algorithm 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 classification algorithms produced using different metrics may predict conflicting outputs for the same input, and that the best metric to use is data-dependent and rarely known while designing the algorithm (Veloso et al. Competence–conscious associative classification. Stati Anal Data Min 2(5–6):361–377,2009; The metric dillema: competence–conscious associative classification. In: Proceeding of the SIAM Data Mining Conference (SDM). SIAM, 2009). This uncertainty concerning the optimal match between metrics and problems is a dilemma, and prevents associative classification algorithms to achieve their maximal performance . A possible solution to this dilemma is to exploit the competence, expertise, or assertiveness of classification algorithms produced using different metrics. The basic idea is that each of these algorithms has a specific sub-domain for which it is most competent (i.e., there is a set of inputs for which this algorithm consistently provides more accurate predictions than algorithms produced using other metrics). Particularly, we investigate stacking -based meta-learning methods, which use the training data to find the domain of competence of associative classification algorithms produced using different metrics. The result is a set of competing algorithms that are produced using different metrics. The ability to detect which of these algorithms is the most competent one for a given input leads to new algorithms , which are denoted as competence–conscious associative classification algorithms .