DIMACS TR: 2002-49

Comprehensive vs. Comprehensible Classifiers in Logical Analysis of Data

Authors: Gabriela Alexe, Sorin Alexe, Peter L. Hammer, and Alexander Kogan


The main objective of this paper is to compare the classification accuracy provided 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 datasets. 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.

Paper Available at: ftp://dimacs.rutgers.edu/pub/dimacs/TechnicalReports/TechReports/2002/2002-49.ps.gz
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