Tahir, Muhammad, Kittler, Josef and Bouridane, Ahmed (2016) Multi-label classification using stacked spectral kernel discriminant analysis. Neurocomputing, 171. pp. 127-137. ISSN 0925-2312
Full text not available from this repository. (Request a copy)Abstract
Multi-label classification is a challenging research problem due to the fact that each example may belong to a varying number of classes. This problem can be further aggravated by high dimensionality and complex correlation among labels. In this paper, a discriminant approach to multi-label classification is proposed using the concept of stacking and spectral regression based kernel discriminant analysis (SSRKDA). For effective stacked generalisation, a novel fast implementation of the leave-one-out cross-validation for SSRKDA is also presented in this paper. The proposed system is validated on several multi-label databases. The results indicate a significant boost in performance when SSRKDA is compared to other multi-label classification techniques.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Multilabel classification; Stacked Kernel Discriminant Analysis; Leave-one-out Cross Validation; Multi-label Nearest Neighbour Classifier |
Subjects: | G400 Computer Science |
Department: | Faculties > Engineering and Environment > Computer and Information Sciences |
Depositing User: | Ay Okpokam |
Date Deposited: | 28 Jul 2015 09:02 |
Last Modified: | 17 Nov 2020 12:07 |
URI: | http://nrl.northumbria.ac.uk/id/eprint/23452 |
Downloads
Downloads per month over past year