Multi-view cost-sensitive kernel learning for imbalanced classification problem

Abstract

Multi-view imbalanced learning concentrates on recognizing valuable patterns from multi-view imbalanced data. There are numerous algorithm-level multi-view imbalanced learning methods including multi-view ensemble learning methods and multi-view cost-sensitive learning methods. However, these approaches have two drawbacks. Firstly, from the multi-view representation perspective, they either ignore the agreement among different views, or fail to fully exploit the complementarity information contained in multi-view data. Secondly, from the class-imbalanced perspective, multi-view ensemble learning first needs to design a specific data pre-processing scheme to generate views and then fuses the local and global information with multi-view ensemble schemes. Such models are closely related to practical problems and lack universality. In contrast, multi-view cost-sensitive learning methods possess concise formulations. But the success of such methods depends on imposing appropriate misclassification cost to the majority and minority classes. Inspired by elegant merits of asymmetric LINEX loss function, we propose a multi-view cost-sensitive kernel learning method with LINEX loss termed as MVCSKL. It not only fully exploits the consensus and complementary information of multiple views, but also adaptively learns and adjusts the misclassification cost of different classes through the asymmetric LINEX loss function. Besides, the introduction of the kernel function enables the MVCSKL model to handle complex nonlinear problem and further guarantees its prediction performance. Furthermore, we adopt the alternating direction method of multipliers and gradient descent algorithm to solve MVCSKL. Based on the Rademacher complexity, we analyze the generalization capability of MVCSKL. Extensive experiments are conducted to verify the effectiveness of the proposed model.