Title: A classification algorithm based on weighted ML-kNN for multi-label data

Authors: Ming Jiang; Lian Du; Jianping Wu; Min Zhang; Zexin Gong

Addresses: Institute of Software and Intelligent Technology, Hangzhou Dianzi University, Hangzhou 310018, China ' Institute of Software and Intelligent Technology, Hangzhou Dianzi University, Hangzhou 310018, China ' Institute of Software and Intelligent Technology, Hangzhou Dianzi University, Hangzhou 310018, China ' Institute of Software and Intelligent Technology, Hangzhou Dianzi University, Hangzhou 310018, China ' Institute of Software and Intelligent Technology, Hangzhou Dianzi University, Hangzhou 310018, China

Abstract: The ML-kNN algorithm uses naive Bayesian classification to modify the traditional kNN algorithm to solve multi-label classification problems. However, the ML-kNN algorithm is prone to misjudgement or incomplete judgment of the unseen instance's label set in two special cases: when the number of labels in the training set is not balanced and when the training instances are unevenly distributed in space. Therefore, a weighted ML-kNN algorithm (i.e., wML-kNN) is proposed in this paper. The main idea is to assign different weights to each label according to the proportion of labels and mutual information of the spatial distribution of unseen instances to training instances. This method can reduce the probability of misjudgement of the unseen instance's label set. A comparative study was conducted on four multi-label datasets that included review classification and three other published benchmark multi-label datasets: yeast gene function analysis, natural scene classification, and musical sentiment classification. The results show that the performance of the wML-kNN algorithm is better than the other four multi-label learning algorithms, including ML-kNN.

Keywords: multi-label learning; weighted multi-label kNN; wML-kNN; k-nearest neighbour; ML-kNN.

DOI: 10.1504/IJIMS.2019.103861

International Journal of Internet Manufacturing and Services, 2019 Vol.6 No.4, pp.326 - 342

Received: 01 Dec 2017
Accepted: 25 May 2018

Published online: 28 Nov 2019 *

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