Machine Learning Through Data Classification and Reduction

Abstract

This paper presents an approach to incremental concept learning in attribute-value systems. The main characteristic feature of this approach is adaptive creation of a complete decision table rather than classification rules. The approach involves gradual accumulation of atomic class descriptions followed by subsequent analysis and simplification of the learned decision table using the ideas of rough sets. Both deterministic and probabilistic aspects of learning are discussed. The basic learning procedure is presented. The convergence of the learning process is illustrated with a computational example using the Thyroid data collection.