Abstract
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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Alsolami, F., Chikalov, I., Moshkov, M. (2013). Sequential Optimization of Approximate Inhibitory Rules Relative to the Length, Coverage and Number of Misclassifications. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds) Rough Sets and Knowledge Technology. RSKT 2013. Lecture Notes in Computer Science(), vol 8171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41299-8_15
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DOI: https://doi.org/10.1007/978-3-642-41299-8_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41298-1
Online ISBN: 978-3-642-41299-8
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