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In the classification of a class imbalance dataset, the performance measureused for the model selection and comparison to competing methods is a majorissue. In order to overcome this problem several performance measures aredefined and analyzed in several perspectives regarding in particular theimbalance ratio. There is still no clear indication which metric is universaland can be used for any skewed data problem. In this paper we introduced a newperformance measure based on the harmonic mean of Recall and Selectivitynormalized in class labels. This paper shows that the proposed performancemeasure has the right properties for the imbalanced dataset. In particular, inthe space defined by the majority class examples and imbalance ratio it is lesssensitive to changes in the majority class and more sensitive to changes in theminority class compared with other existing single-value performance measures.Additionally, the identity of the other performance measures has been provenanalytically.

Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels