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Sequential Optimization of Approximate Inhibitory Rules Relative to the Length, Coverage and Number of Misclassifications
Conference proceeding   Peer reviewed

Sequential Optimization of Approximate Inhibitory Rules Relative to the Length, Coverage and Number of Misclassifications

Fawaz Alsolami, Igor Chikalov and Mikhail Moshkov
ROUGH SETS AND KNOWLEDGE TECHNOLOGY: 8TH INTERNATIONAL CONFERENCE, Vol.8171, pp.154-165
Lecture Notes in Artificial Intelligence
01/01/2013
DOI: https://doi.org/10.1007/978-3-642-41299-8_15

Abstract

Computer Science Computer Science, Artificial Intelligence Computer Science, Information Systems Science & Technology Technology
This paper is devoted to the study of algorithms for sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications. Theses algorithms are based on extensions of dynamic programming approach. The results of experiments for decision tables from UCI Machine Learning Repository are discussed.

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