Comprehensive vs. comprehensible classifiers in logical analysis of data

Authors:
Gabriela Alexe;Sorin Alexe;Peter L. Hammer;Alexander Kogan
Affiliations:
RUTCOR Rutgers, The State University of New Jersey, 640 Bartholomew Road, Piscataway, NJ 08854-8003, USA;RUTCOR Rutgers, The State University of New Jersey, 640 Bartholomew Road, Piscataway, NJ 08854-8003, USA;RUTCOR Rutgers, The State University of New Jersey, 640 Bartholomew Road, Piscataway, NJ 08854-8003, USA;RUTCOR Rutgers, The State University of New Jersey, 640 Bartholomew Road, Piscataway, NJ 08854-8003, USA
Venue:
Discrete Applied Mathematics
Year:
2008

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Abstract

The main objective of this paper is to compare the classification accuracy provided by large, comprehensive collections of patterns (rules) derived from archives of past observations, with that provided by small, comprehensible collections of patterns. This comparison is carried out here on the basis of an empirical study, using several publicly available data sets. The results of this study show that the use of comprehensive collections allows a slight increase of classification accuracy, and that the ''cost of comprehensibility'' is small.