Improved shrunken centroid classifiers for high-dimensional class-imbalanced data
نویسندگان
چکیده
منابع مشابه
Class-imbalanced classifiers for high-dimensional data
A class-imbalanced classifier is a decision rule to predict the class membership of new samples from an available data set where the class sizes differ considerably. When the class sizes are very different, most standard classification algorithms may favor the larger (majority) class resulting in poor accuracy in the minority class prediction. A class-imbalanced classifier typically modifies a ...
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MOTIVATION The nearest shrunken centroid (NSC) method has been successfully applied in many DNA-microarray classification problems. The NSC uses 'shrunken' centroids as prototypes for each class and identifies subsets of genes that best characterize each class. Classification is then made to the nearest (shrunken) centroid. The NSC is very easy to implement and very easy to interpret, however, ...
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In multi-instance learning, each learning object consists of many descriptive instances. In the corresponding classification problems, each training object is labeled, but its constituent instances are not. The classification objective is to predict the class label of unseen objects. As in traditional single-instance classification, when the class sizes of multi-instance data are imbalanced, cl...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2013
ISSN: 1471-2105
DOI: 10.1186/1471-2105-14-64