Abstract
The paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to length, coverage and number of misclassifications. Presented algorithm constructs a directed acyclic graph \({\varDelta }_{\gamma }(T)\) which nodes are subtables of the decision table T. Based on the graph \({\varDelta }_{\gamma }(T)\) we can describe all irredundant \(\gamma \)-decision rules with the minimum length, after that among these rules describe all rules with the maximum coverage, and among such rules describe all rules with the minimum number of misclassifications. We can also change the set of cost functions and order of optimization. Sequential optimization can be considered as a tool that helps to construct simpler rules for understanding and interpreting by experts.
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The author would like to thank you Prof. Mikhail Moshkov, Dr. Igor Chikalov and Talha Amin for possibility to use Dagger software system.
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Zielosko, B. (2015). Sequential Optimization of \(\gamma \)-Decision Rules Relative to Length, Coverage and Number of Misclassifications. In: Peters, J., Skowron, A., Ślȩzak, D., Nguyen, H., Bazan, J. (eds) Transactions on Rough Sets XIX. Lecture Notes in Computer Science(), vol 8988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47815-8_5
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DOI: https://doi.org/10.1007/978-3-662-47815-8_5
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