Fuzzy classifiers versus cost-based Bayes classifiers

Learning classifiers for imbalanced data sets is a difficult task for current machine learning algorithms. The difficulty can be traced to the fact that being accuracy driven, most algorithms lead to classifiers which are biased towards the majority class. Introducing in the learning algorithm miscl...

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Bibliographic Details
Published inNAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society pp. 302 - 305
Main Authors Ralescu, A., Visa, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2006
Subjects
Bayesian methods
Classification tree analysis
cost-based learning
Costs
Equations
Frequency
fuzzy classifiers
Fuzzy sets
Linear matrix inequalities
Machine learning
Machine learning algorithms
Sampling methods
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Summary:Learning classifiers for imbalanced data sets is a difficult task for current machine learning algorithms. The difficulty can be traced to the fact that being accuracy driven, most algorithms lead to classifiers which are biased towards the majority class. Introducing in the learning algorithm misclassification costs, which differentiate between classes, has gone a long way towards improving the performance of the resulting classifiers. Alternatively, experiments have shown that a particular type of fuzzy classifiers apply better for imbalanced data sets. This paper explores the hypothesis that fuzzy classifiers can account to a certain extent for the error costs associated with other learning algorithms
ISBN:1424403626
9781424403622
DOI:10.1109/NAFIPS.2006.365426