Classification with reject option in gene expression data

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Classification with reject option in gene expression data

MOTIVATION The classification methods typically used in bioinformatics classify all examples, even if the classification is ambiguous, for instance, when the example is close to the separating hyperplane in linear classification. For medical applications, it may be better to classify an example only when there is a sufficiently high degree of accuracy, rather than classify all examples with dec...

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Classification is one of the most important tasks of machine learning. Although the most well studied model is the two-class problem, in many scenarios there is the opportunity to label critical items for manual revision, instead of trying to automatically classify every item. In this paper we adapt a paradigm initially proposed for the classification of ordinal data to address the classificati...

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We consider multi-label classification problems in application scenarios where classifier accu-racy is not satisfactory, but manual annotation is too costly. In single-label problems, a wellknown solution consists of using a reject option, i.e., allowing a classifier to withhold unreliabledecisions, leaving them (and only them) to human operators. We argue that this solution can be<...

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ژورنال

عنوان ژورنال: Bioinformatics

سال: 2008

ISSN: 1367-4803,1460-2059

DOI: 10.1093/bioinformatics/btn349