Examplar-based prototype selection for a multi-strategy learning system

Abstract

Multistrategy learning (MSL) consists of combining at least two different learning strategies to bring out a powerful system, where the drawbacks of the basic algorithms are avoided. In this scope, instance-based learning (IBL) techniques are often used as the basic component. However, one of the major drawbacks of IBL is the prototype selection problem which consists in selecting a subset of representative instances in order to reduce the classification process. This paper presents a novel approach which consists of three steps. The first one builds a set of lattice-based hypotheses that characterize the training data set. Given an unseen example, the second step selects a subset of training instances through the way they verify the same hypotheses as the unseen example. Finally the last step uses this subset of training instances as the prototypes for the classification of the unseen example. Results of experiments that we conducted show the effectiveness of our approach compared to standard ML techniques on different datasets.