Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data

Abstract

Learning from imbalanced data, where the number of observations in one class is significantly rarer than in other classes, has gained considerable attention in the data mining community. Most existing literature focuses on binary imbalanced case while multi-class imbalanced learning is barely mentioned. What's more, most proposed algorithms treated all imbalanced data consistently and aimed to handle all imbalanced data with a versatile algorithm. In fact, the imbalanced data varies in their imbalanced ratio, dimension and the number of classes, the performances of classifiers for learning from different types of datasets are different. In this paper we propose an adaptive multiple classifier system named of AMCS to cope with multi-class imbalanced learning, which makes a distinction among different kinds of imbalanced data. The AMCS includes three components, which are, feature selection, resampling and ensemble learning. Each component of AMCS is selected discriminatively for different types of imbalanced data. We consider two feature selection methods, three resampling mechanisms, five base classifiers and five ensemble rules to construct a selection pool, the adapting criterion of choosing each component from the selection pool to frame AMCS is analyzed through empirical study. In order to verify the effectiveness of AMCS, we compare AMCS with several state-of-the-art algorithms, the results show that AMCS can outperform or be comparable with the others. At last, AMCS is applied in oil-bearing reservoir recognition. The results indicate that AMCS makes no mistake in recognizing characters of layers for oilsk81-oilsk85 well logging data which is collected in Jianghan oilfield of China.