ML2S-SVM: multi-label least-squares support vector machine classifiers
ISSN: 0264-0473
Article publication date: 22 November 2019
Issue publication date: 22 November 2019
Abstract
Purpose
Image classification is becoming a supporting technology in several image-processing tasks. Due to rich semantic information contained in the images, it is very popular for an image to have several labels or tags. This paper aims to develop a novel multi-label classification approach with superior performance.
Design/methodology/approach
Many multi-label classification problems share two main characteristics: label correlations and label imbalance. However, most of current methods are devoted to either model label relationship or to only deal with unbalanced problem with traditional single-label methods. In this paper, multi-label classification problem is regarded as an unbalanced multi-task learning problem. Multi-task least-squares support vector machine (MTLS-SVM) is generalized for this problem, renamed as multi-label LS-SVM (ML2S-SVM).
Findings
Experimental results on the emotions, scene, yeast and bibtex data sets indicate that the ML2S-SVM is competitive with respect to the state-of-the-art methods in terms of Hamming loss and instance-based F1 score. The values of resulting parameters largely influence the performance of ML2S-SVM, so it is necessary for users to identify proper parameters in advance.
Originality/value
On the basis of MTLS-SVM, a novel multi-label classification approach, ML2S-SVM, is put forward. This method can overcome the unbalanced problem but also explicitly models arbitrary order correlations among labels by allowing multiple labels to share a subspace. In addition, the multi-label classification approach has a wider range of applications. That is to say, it is not limited to the field of image classification.
Keywords
Acknowledgements
This research received the financial support from Social Science Foundation of Beijing Municipality under grant number 17GLB074. Our gratitude also goes to the anonymous reviewers for their valuable comments.
Citation
Xu, S. and An, X. (2019), "ML2S-SVM: multi-label least-squares support vector machine classifiers", The Electronic Library, Vol. 37 No. 6, pp. 1040-1058. https://doi.org/10.1108/EL-09-2019-0207
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited