A Neighborhood Model with Both Distance and Quantity Constraints for Multilabel Data. (19th September 2022)

• Record Type: Journal Article
• Authors: Jiang, Xiaoli; Zhou, Jing; Qiao, Xinyue; Peng, Chang; Su, Shiwen
• Other Names: Yi Yugen Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-09-19
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/9891971
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : In this paper, a novel distance-based multilabel classification algorithm is proposed. The proposed algorithm combines k-nearest neighbors (kNN) with neighborhood classifier (NC) to impose double constraints on the quantity and distance of the neighbors. In short, the radius constraint is introduced in the kNN model to improve the classification accuracy, and the quantity constraint k is added in the NC model to speed up computing. From the neighbors with the double constraints, the probabilities for each label are estimated by the Bayesian rule, and the classification judgment is made according to the probabilities. Experimental results show that the proposed algorithm has slight advantages over similar algorithms in calculation speed and classification accuracy.