Abstract
In the paper, we present a comparison of dynamic programming and greedy approaches for construction and optimization of approximate decision rules relative to the number of misclassifications. We use an uncertainty measure that is a difference between the number of rows in a decision table T and the number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules that localize rows in subtables of T with uncertainty at most γ. Experimental results with decision tables from the UCI Machine Learning Repository are also presented.
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Amin, T., Chikalov, I., Moshkov, M., Zielosko, B. (2013). Optimization of Approximate Decision Rules Relative to Number of Misclassifications: Comparison of Greedy and Dynamic Programming Approaches. In: Graña, M., Toro, C., Howlett, R.J., Jain, L.C. (eds) Knowledge Engineering, Machine Learning and Lattice Computing with Applications. KES 2012. Lecture Notes in Computer Science(), vol 7828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37343-5_5
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DOI: https://doi.org/10.1007/978-3-642-37343-5_5
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