B. Zupan, J. A. Halter, and M. Bohanec:
Concept Discovery by Decision Table Decomposition and
its Application in Neurophysiology
in
N. Lavrac, E. Keravnou, B. Zupan, editors,
Intelligent Data Analysis in Medicine and Pharmacology, Kluwer, 1997.
Abstract
This chapter presents a ``divide-and-conquer'' data analysis method
that, given a concept described by a decision table, it 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.