Concept representation with overlapping feature intervals

This article presents a new form of exemplar-based learning method, based on overlapping feature intervals. In this model, a concept is represented by a collection of overlappling intervals for each feature and class. Classifica-tion with Overlapping Feature Intervals s COFI. is a particular implementation of this technique. In this incremental, inductive, and supervised learning method, the basic unit of the representation is an interval. The COFI algorithm learns the projections of the intervals in each feature dimension for each class. Initially, an interval is a point on a feature-class dimension; then it can be expanded through generalization. No specialization of intervals is done on feature-class dimensions by this algorithm. Classification in the COFI algorithm is based on a majority voting among the local predictions that are made individually by each feature. An evaluation of COFI and its comparison with similar other classification techniques is give n. Learning refers to a wide spectrum of situations in which a learner increases his knowledge or skill in accomplishing certain tasks. The learner applies inferences to some material in order to construct an appropriate representation of some relevant aspect of reality. Th