An Improved Measurement of the Imbalanced Dataset

Imbalanced classification is a classification problem that violates the assumption of uniform distribution of samples. In such problems, traditional imbalanced datasets are measured in terms of the imbalance of sample size, without considering the distribution information, which has a more important impact on the classification performance, so the traditional measurements have a weak relation with the classification performance. This paper proposed an improved measurement for imbalanced datasets, it is based on the idea that a sample surrounded by more same class samples is easier to classify, for each sample of different classes, the proposed method calculates the average number of the k nearest neighbors in the same class in different subsets under the weighted k-NN, after that, the product of these average values is regarded as the measurement of this dataset, and it is a good indicator of the relationship between the distribution of samples and the classification results. The experimental results show that the proposed measurement has a higher correlation with the classification results and shows the difficulty of classification of data sets more clearly.