A Generic Framework for Rule-Based Classification

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 HARMONY 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.