Partial Multilabel Learning Using Fuzzy Neighborhood-Based Ball Clustering and Kernel Extreme Learning Machine
Lin Sun, Tianxiang Wang, Weiping Ding, Jiucheng Xu
Abstract
Partial multilabel learning (PML) has attracted considerable interest from scholars. Most PML models construct objective functions and optimize target parameters, which add noise to the training process and results in a poor classification effect. In addition, feature correlation is limited to linear transformations while ignoring the complex relationships among features. In this study, we develop a novel PML model with fuzzy neighborhood-based ball clustering and kernel extreme learning machine (KELM). To reduce the interference from noise, ball <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering is introduced to preprocess partial multilabel data and initialize ball clustering. A new ball clustering model based on fuzzy neighborhood is first designed to address partial multilabel systems. The particle-ball fusion strategy is developed to merge the particle balls reasonably, and the fuzzy membership function and label enhancement are studied for the subsequent training process. Then novel KELM with feature transformation matrix of training data is produced to analyze the nonlinear relationships among features. Finally, a nonsmooth convex objective function with the regression model is constructed to analyze the complex nonlinear relationships among features, and the optimal solutions of three objective parameters are solved by accelerated proximal gradient optimization. Experiments on 14 datasets reveal the effectiveness of the developed algorithm.