An online weighted sequential extreme learning machine for class imbalanced data streams

Abstract

Abstract When general online classification algorithms deal with imbalanced data streams, there are always some problems, such as over fitting phenomenon caused by insufficient simple learning and instability of training model. In this paper, we introduce online sequential extreme learning machine (OSELM) as the basic theory model, and combine with the cost-sensitive strategy, then propose a cost-sensitive learning based online sequential extreme learning machine algorithm (C-OSELM). Firstly, in order to solve the problem that minority classes are easily misclassified due to class imbalance, use cost-sensitive strategy, by assigning different penalty parameters to various samples, a weighting matrix is constructed to improve the misclassification cost, thereby effectively alleviating the excessive deviation of decision surface. On this basis, in order to solve the problem that the penalty parameter is too single and the algorithm is not universal, the cost adjustment function is introduced to optimize the weight parameters to select the appropriate weight. Finally, 16 class II imbalanced datasets are used for comparison and verification. The experimental results show that the classification performances of the proposed C-OSELM algorithm are better than other comparative algorithms.