A Generic Framework for Rule-Based Classification
Résumé
Classification is an important field of data mining problems. Given a set of labeled training examples the classification task constructs a classifier. A classifier is a global model which is used to predict the class label for data objects that are unlabeled. Many approaches have been proposed for the classification problem. Among them, rule-induction, associative and instance-centric approaches have been closely integrated with constraint-based data mining. There also exist several classification methods based on each of these approaches, e.g. AQ, CBA and HAR-MONY respectively. Moreover, each classification method may provide one or more algorithms that exploit particular local pattern extraction techniques to construct a classifier. In this paper, we proposed a generic classification framework that encompasses all the mentioned approaches. Based on our framework we present a formal context to define basic concepts, operators for classifier construction, composition of classifiers, and class prediction. Moreover, we proposed a generic classifier construction algorithm (ICCA) that incrementally constructs a classifier using the proposed operators. This algorithm is generic in the sense that it can uniformly represent a large class of existing classification algorithms. We also present the properties under which different optimization possibilities are provided in the generic algorithm.