Concept Discovery by Decision Table Decomposition and its Application in Neurophysiology

Abstract

This chapter presents a ``divide-and-conquer{''} data analysis method that, given a concept described by a decision table, develops its description in terms of intermediate concepts described by smaller and more manageable decision tables. The method is based on decision table decomposition, a machine learning approach that decomposes a given decision table into an equivalent hierarchy of decision tables. The decomposition aims to discover the decision tables that are overall less complex than the initial one, potentially easier to interpret, and introduce new and meaningful intermediate concepts. The chapter introduces the decomposition method and, through decomposition-based data analysis of two neurophysiological datasets, shows that the decomposition can discover physiologically meaningful concept hierarchies and construct interpretable decision tables which reveal relevant physiological principles.