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Synthetic Minority Oversampling TEchnique (SMOTE) is a popular oversampling method that was proposed to improve random oversampling but its behavior on high-dimensional data has not been thoroughly investigated. In this paper we evaluate the performance of SMOTE on high-dimensional data, using gene expression microarray data. We observe that SMOTE does not attenuate the bias towards the classification...
The goal of class prediction studies is to develop rules to accurately predict the class membership of new subjects. The classifiers differ in the way they combine the values of the variables available for each subject. Frequently the classifiers are developed using class-imbalanced data, where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced...
In large software projects, software quality prediction is an important aspect of the development cycle to help focus quality assurance efforts on the modules most likely to contain faults. To perform software quality prediction, various software metrics are collected during the software development cycle, and models are built using these metrics. However, not all features (metrics) make the same...
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