Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data
نویسندگان
چکیده
منابع مشابه
Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data
Cost-sensitive classification is based on a set of weights defining the expected cost of misclassifying an object. In this paper, a Genetic Fuzzy Classifier, which is able to extract fuzzy rules from interval or fuzzy valued data, is extended to this type of classification. This extension consists in enclosing the estimation of the expected misclassification risk of a classifier, when assessed ...
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Designing classifiers may follow different goals. Which goal to prefer among others depends on the given cost situation and the class distribution. For example, a classifier designed for best accuracy in terms of misclassifications may fail when the cost of misclassification of one class is much higher than that of the other. This paper presents a decision-theoretic extension to make fuzzy rule...
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Determining whether an imprecise dataset is imbalanced is not immediate. The vagueness in the data causes that the prior probabilities of the classes are not precisely known, and therefore the degree of imbalance can also be uncertain. In this paper we propose suitable extensions of different resampling algorithms that can be applied to interval valued, multi-labelled data. By means of these ex...
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ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2011
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2011.02.007