An Integrated Characterization and Discrimination Scheme to Improve Learning Efficiency in Large Data Sets

Abstract

This work proposes a learning scheme which integrates Characterization and Discrimination activities with the aim of improving learning efficiency in large data sets. Characterization is considered to be a process which builds up a rough concept description using only positive examples. This description already excludes most of the extreme negative examples. Discrimination is considered to be an incremental learning process which begins with the characteristic description and refines it so as to make it consistent with the negative examples (near misses) which are still covered. During this phase learning efficiency is greatly improved by considering only near misses as counterexamples. Finally, the description is simplified by dropping some characterizing but not discriminant parts of the description. This learning scheme is discussed and compared with the traditional data reduction techniques. Some experimental results are reported which show the gain in efficiency obtained, particularly on real applicative domains.