Classifiers Based on Optimal Decision Rules

• Autorzy: Amin T. , Chikalov I. , Moshkov M. , Zielosko B.
• Języki publikacji: EN

Abstrakty

EN

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).

Słowa kluczowe

EN

classifiers; decision rules; dynamic programming

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2013
• Tom: Vol. 127, nr 1-4
• Strony: 151--160
• Opis fizyczny: Bibliogr. 17 poz., tab.

Twórcy

autor

Amin T.

autor

Chikalov I.

autor

Moshkov M.

autor

Zielosko B.

• Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia

Bibliografia

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