Classification Rule Discovery with Ant Colony Optimization
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications
Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications
An evolutionary memetic algorithm for rule extraction
Expert Systems with Applications: An International Journal
Order based genetic algorithms for the search of approximate entropy reducts
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
ENDER: a statistical framework for boosting decision rules
Data Mining and Knowledge Discovery
Sequential covering rule induction algorithm for variable consistency rough set approaches
Information Sciences: an International Journal
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Optimization of inhibitory decision rules relative to length and coverage
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Dynamic programming approach to optimization of approximate decision rules
Information Sciences: an International Journal
Combinatorial Machine Learning: A Rough Set Approach
Combinatorial Machine Learning: A Rough Set Approach
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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.