Author/Editor		Blagus, Rok; Lusa, Lara
Title		Evaluation of SMOTE for high-dimensional class-imbalanced microarray data
Type		članek
Source		In: Tao D, editor. ICMLA 2012. 11th International conference on machine learning and applications; 2012 Dec 12-15; Boca Raton. Institute of electrical and electronics engineers,
Publication year		2012
Volume		str. 89-94
Language		eng
Abstract		Synthetic Minority Oversampling TEchnique (SMOTE) is a popular oversampling method that was proposed to improve random oversampling but its behavior on highdimensional 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 in the majority class for most classifiers, and it is less effective than random undersampling. SMOTE is beneficial for k-NN classifiers based on the Euclidean distance if the number of variables is reduced performing some type of variable selection and the benefit is larger if more neighbors are used. If the variable selection is not performed than the k-NN classification is counter intuitively biased towards the minority class, so SMOTE for k-NN without variable selection should not be used in practice.