ML2S-SVM: multi-label least-squares support vector machine classifiers.
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 s...
| Publicado en: | Electronic Library Vol. 37; no. 6; pp. 1040 - 1059 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Emerald Publishing Limited
2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139808380&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139808380 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02640473 2OJ jtl: Electronic Library issn: 02640473 maglogo: N pubinfo: dt: 2019 vid: 37 iid: 6 pid: 465 pub: Emerald Publishing Limited artinfo: ui: 139808380 139808380 139808380 10.1108/EL-09-2019-0207 139808380 ppf: 1040 ppct: 19 formats: tig: atl: ML2S-SVM: multi-label least-squares support vector machine classifiers. aug: au: Xu, Shuo An, Xin affil: Research Base of Beijing Modern Manufacturing Development, College of Economics and Management, Beijing University of Technology, Beijing, PR China sug: subj: Regression Machine Learning Library Classification Digital Imaging Classification Human Algorithms Minimum Data Set Descriptive Statistics Emotions Motion Pictures Yeasts Funding Source ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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