Online Sequential Learning based on Enhanced Extreme Learning Machine using Left or Right Pseudo-inverse

The class imbalance problem has been reported as an importan t ch llenge in various fields such as Pattern Recognition, Data Mining and Machine Learning. A less explo red research area is related to how to evaluate classifiers on imbalanced data sets. This work analyzes the b ehaviour of performance measures widely used on imbalanced problems, as well as other metrics recently prop osed in the literature. We perform two theoretical analysis based on Pearson correlation and operations for a 2 ×2 confusion matrix with the aim to show the strengths and weaknesses of those performance metrics in th e presence of skewed distributions.

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