Class prediction for high-dimensional class-imbalanced data

@inproceedings{Blagus2010ClassPF,
  title={Class prediction for high-dimensional class-imbalanced data},
  author={Rok Blagus and Lara Lusa},
  booktitle={BMC Bioinformatics},
  year={2010}
}
The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used… CONTINUE READING
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