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Classifiers Based on Optimal Decision Rules.
- Source :
-
Fundamenta Informaticae . 2013, Vol. 127 Issue 1-4, p151-160. 10p. - Publication Year :
- 2013
-
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). [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01692968
- Volume :
- 127
- Issue :
- 1-4
- Database :
- Academic Search Index
- Journal :
- Fundamenta Informaticae
- Publication Type :
- Academic Journal
- Accession number :
- 91257740
- Full Text :
- https://doi.org/10.3233/FI-2013-901