Maximizing benefit of classifications using feature intervals
Date
2003
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Source Title
Knowledge-Based Intelligent Information and Engineering Systems
Print ISSN
0302-9743
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Publisher
Springer, Berlin, Heidelberg
Volume
2773
Issue
Pages
339 - 345
Language
English
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Abstract
There 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.
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Keywords
Algorithms , Classification (of information) , Constraint Theory , Data Mining , Error Analysis , Knowledge Representation , Matrix Algebra , Set Theory , Knowledge Based Systems , Knowledge Representation , Benefit-Maximizing Classifier with Feature Intervals (BMFI) , Cost-Sensitive Classification , Cost-Sensitive Learning , Feature Intervals , Learning Systems , Classification (of information) , Asymmetric Costs , Classification Algorithm , Classification Methods , Cost-Sensitive Algorithm , Feature Projection