Cost-sensitive feature selection for class imbalance problem

Abstract

The class imbalance problem is encountered in real-world applications of machine learning and results in suboptimal performance during data classification. This is especially true when data is not only imbalanced but also high dimensional. The class imbalance is very often accompanied by a high dimensionality of datasets and in such a case these problems should be considered together. Traditional feature selection methods usually assign the same weighting to samples from different classes when the samples are used to evaluate each feature. Therefore, they do not work good enough with imbalanced data. In situation when the costs of misclassification of different classes are diverse, cost-sensitive learning methods are often applied. These methods are usually used in the classification phase, but we propose to take the cost factors into consideration during the feature selection. In this study we analyse whether the use of cost-sensitive feature selection followed by resampling can give good results for mentioned problems. To evaluate tested methods three imbalanced and multidimensional datasets are considered and the performance of chosen feature selection methods and classifiers are analysed.