İkizler, NazlıGüvenir, H. Altay2016-02-082016-02-0820030302-9743http://hdl.handle.net/11693/27510Date of Conference: KES: International Conference on Knowledge-Based and Intelligent Information and Engineering Systems 7th INternational ConferenceDate of Conference: September 2003There is a great need for classification methods that can properly handle asymmetric cost and benefit constraints of classifications. In this study, we aim to emphasize the importance of classification benefits by means of a new classification algorithm, Benefit-Maximizing classifier with Feature Intervals (BMFI) that uses feature projection based knowledge representation. Empirical results show that BMFI has promising performance compared to recent cost-sensitive algorithms in terms of the benefit gained.EnglishAlgorithmsClassification (of information)Constraint TheoryData MiningError AnalysisKnowledge RepresentationMatrix AlgebraSet TheoryKnowledge Based SystemsKnowledge RepresentationBenefit-Maximizing Classifier with Feature Intervals (BMFI)Cost-Sensitive ClassificationCost-Sensitive LearningFeature IntervalsLearning SystemsClassification (of information)Asymmetric CostsClassification AlgorithmClassification MethodsCost-Sensitive AlgorithmFeature ProjectionConference Paper10.1007/978-3-540-45224-9_4810.1007/978-3-540-45224-9