Title: An improved multi-instance multi-label learning algorithm based on representative instances selection and label correlations

Authors: Chanjuan Liu; Tongtong Chen; Hailin Zou; Xinmiao Ding; Yuling Wang

Addresses: Provincial Key Laboratory of Cyber-Physical System and Intelligent Control, School of Information and Electrical Engineering, Ludong University, Yantai 264025, China ' Provincial Key Laboratory of Cyber-Physical System and Intelligent Control, School of Information and Electrical Engineering, Ludong University, Yantai 264025, China ' Provincial Key Laboratory of Cyber-Physical System and Intelligent Control, School of Information and Electrical Engineering, Ludong University, Yantai 264025, China ' School of Information and Electronic Engineering, Shandong Institute of Business and Technology, Yantai 264005, China ' Department of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China

Abstract: Multi-Instance Multi-Label Learning (MIML) has been successfully used in image and text classification problems. It is noteworthy that few of the previous studies consider the pattern-label relations. Inevitably, there are some useless instances in a bag which will reduce the accuracy of the annotation. In this paper we focus on this problem. Firstly, an instance selection method via joint l_2,1-norms constraint is employed to eliminate the useless instances and select the representative instances by modelling the instance correlation. Then, bags are mapped to these representative instances. Finally, the classifier is trained by an optimisation algorithm based on label correlations. Experimental results on image data set, text data sets and bird song audio data set show that the proposed algorithm significantly improves the performance of MIML classifier compared with the state-of-the-art methods.

Keywords: multi-instance multi-label learning; representative instances selection; joint l_2,1-norms constraint; label correlations.

DOI: 10.1504/IJGUC.2018.093955

International Journal of Grid and Utility Computing, 2018 Vol.9 No.3, pp.268 - 277

Received: 17 Dec 2015
Accepted: 05 Jun 2016
Published online: 10 Aug 2018 *

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