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- W1544147595 abstract "Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: i which rules are better from the point of view of classification --exact or approximate; and ii which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization length+coverage or coverage+length is better than the ordinary optimization length or coverage." @default.
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- W1544147595 date "2013-01-01" @default.
- W1544147595 modified "2023-09-23" @default.
- W1544147595 title "Classifiers Based on Optimal Decision Rules" @default.
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- W1544147595 doi "https://doi.org/10.3233/fi-2013-901" @default.
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