Multi-view evidential K-NN classification

Abstract

Multi-view classification, aiming to classify samples represented by multiple feature vectors, has become a hot topic in pattern recognition. Although many methods with promising performances have been proposed, their practicality is still limited by the lack of interpretability in some situations. Besides, an appropriate description for the soft labels of multi-view samples is missing, which may degrade the classification performance, especially for those samples located in highly-overlapping areas of multiple vector spaces. To address these issues, we extend the K-nearest neighbor (K-NN) classification algorithm to multi-view learning, under the theoretical framework of evidence theory. The learning process is formalized, firstly, as an optimization problem, where the weights of different views, an adaptive K value of every sample and the distance matrix are determined jointly based on training error. Then, the final classification result is derived according to the philosophy of the evidential K-NN classification algorithm. Detailed ablation studies demonstrate the benefits of the joint learning for adaptive neighborhoods and view weights in a supervised way. Comparative experiments on real-world datasets show that our algorithm performs better than other state-of-the-art methods. A real-world industrial application for condition monitoring shown in Appendix F exemplifies the need to use the evidence theory and the benefits from the unique interpretability of K-NN in detail.