  In the classification of a class imbalance dataset, the performance measure
used for the model selection and comparison to competing methods is a major
issue. In order to overcome this problem several performance measures are
defined and analyzed in several perspectives regarding in particular the
imbalance ratio. There is still no clear indication which metric is universal
and can be used for any skewed data problem. In this paper we introduced a new
performance measure based on the harmonic mean of Recall and Selectivity
normalized in class labels. This paper shows that the proposed performance
measure has the right properties for the imbalanced dataset. In particular, in
the space defined by the majority class examples and imbalance ratio it is less
sensitive to changes in the majority class and more sensitive to changes in the
minority class compared with other existing single-value performance measures.
Additionally, the identity of the other performance measures has been proven
analytically.
