Multi-label Classification based on Particle Swarm Algorithm
Qingzhong Liang, Ze Wang, Yuanyuan Fan, Chao Liu, Xuesong Yan, Chengyu Hu, Hong Liang Yao · 2013
Multi-label classification is a generalization of single-label classification, and its samples belong to multiple labels. The K-nearest neighbor algorithm can solve this problem as an optimization problem. It finds the optimum solution by caculating the distance between each sample in general. But in fact, the distance of K-nearest neighbor algorithm may be miscalculated due to the caused by the redundant or irrelevant characteristic value. In order to solve this problem, in this paper, we propose a novel method that uses the particle swarm algorithm to optimize the feature weights to improve the accuracy of distance calculation. As a result, it can improve classification accuracy further. The experimental results show that applying particle swarm algorithm's optimization technique to improving K-nearest neighbor algorithm for multi-label classification problem, can improve the accuracy of classification effectively.