Maximizing benefit of classifications using feature intervals

Date

2003

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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.

Source Title

Knowledge-Based Intelligent Information and Engineering Systems

Publisher

Springer, Berlin, Heidelberg

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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

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http://hdl.handle.net/11693/27510

Published Version (Please cite this version)

https://doi.org/10.1007/978-3-540-45224-9_48
 https://doi.org/10.1007/978-3-540-45224-9

Collections

Scholarly Publications - Computer Engineering

Language

English

Type

Conference Paper