Optimization of β-decision rules relative to number of misclassifications

Authors:
Beata Marta Zielosko
Affiliations:
Mathematical and Computer Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, Institute of Computer Science, University of Silesia, Sosno ...
Venue:
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
Year:
2012

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Abstract

In the paper, we present an algorithm for optimization of approximate decision rules relative to the number of misclassifications. The considered algorithm is based on extensions of dynamic programming and constructs a directed acyclic graph Δβ(T). Based on this graph we can describe the whole set of so-called irredundant β-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented.