Weighted one-class classification for different types of minority class examples in imbalanced data

Imbalanced classification is one of the most challenging machine learning problem. Recent studies show, that often the uneven ratio of objects in classes is not the biggest factor, determining the drop of classification accuracy. It is also related to some difficulties embedded in the nature of the data. In this paper we study the different types of minority class examples and distinguish four groups of objects - safe, borderline, rare and outliers. To deal with the imbalance problem, we use a one-class classification, that is focused on a proper identification of the minority class samples. We further augment this model by incorporating the knowledge about the minority object types in the training dataset. This is done applying weighted one-class classifier and adjusting weights assigned to minority class objects, depending on their type. A strategy for calculating the new weights for minority examples is proposed. Experimental analysis, carried on a set of benchmark datasets, confirms that the proposed model can achieve a satisfactory recognition rate and often outperform other state-of-the-art methods, dedicated to the imbalanced classification.