Representative multi-label Bayesian approach for image classification

Zhiwen Yu; Xiaowei Wang; Jia You; Guoqiang Han; Le Li

doi:10.1109/ICMLC.2012.6359569

Representative multi-label Bayesian approach for image classification

Zhiwen Yu, Xiaowei Wang, Jia You, Guoqiang Han, Le Li

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

Recently, multi-label learning approaches are gaining more and more attention due to its useful applications in the area of data mining and bioinformatics. Though there exist a lot of multi-label learning approaches, few of them consider how to deal with the dataset with noisy attributes. In this paper, we will present Representative Multi-Label Bayesian Approach (RMLBA) to process the dataset with noisy attributes. RMLBA incorporates the affinity propagation (AP) approach and the Bayesian approach into the multi-label learning framework. Instead of considering all the attributes, RMLBA only focuses on a small subset of representative attributes which is detected by the AP. The experiments on image classification illustrate the RMLBA works well for the multi-label classification problems.

Original language	English
Title of host publication	Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Pages	1395-1399
Number of pages	5
Volume	4
DOIs	https://doi.org/10.1109/ICMLC.2012.6359569
Publication status	Published - 31 Dec 2012
Event	2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 - Xian, Shaanxi, China Duration: 15 Jul 2012 → 17 Jul 2012

Conference

Conference	2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Country/Territory	China
City	Xian, Shaanxi
Period	15/07/12 → 17/07/12

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Computer Networks and Communications
Human-Computer Interaction

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10.1109/ICMLC.2012.6359569

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title = "Representative multi-label Bayesian approach for image classification",

abstract = "Recently, multi-label learning approaches are gaining more and more attention due to its useful applications in the area of data mining and bioinformatics. Though there exist a lot of multi-label learning approaches, few of them consider how to deal with the dataset with noisy attributes. In this paper, we will present Representative Multi-Label Bayesian Approach (RMLBA) to process the dataset with noisy attributes. RMLBA incorporates the affinity propagation (AP) approach and the Bayesian approach into the multi-label learning framework. Instead of considering all the attributes, RMLBA only focuses on a small subset of representative attributes which is detected by the AP. The experiments on image classification illustrate the RMLBA works well for the multi-label classification problems.",

author = "Zhiwen Yu and Xiaowei Wang and Jia You and Guoqiang Han and Le Li",

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doi = "10.1109/ICMLC.2012.6359569",

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volume = "4",

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Yu, Z, Wang, X, You, J, Han, G & Li, L 2012, Representative multi-label Bayesian approach for image classification. in Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012. vol. 4, 6359569, pp. 1395-1399, 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012, Xian, Shaanxi, China, 15/07/12. https://doi.org/10.1109/ICMLC.2012.6359569

Representative multi-label Bayesian approach for image classification. / Yu, Zhiwen; Wang, Xiaowei; You, Jia et al.
Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012. Vol. 4 2012. p. 1395-1399 6359569.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - Recently, multi-label learning approaches are gaining more and more attention due to its useful applications in the area of data mining and bioinformatics. Though there exist a lot of multi-label learning approaches, few of them consider how to deal with the dataset with noisy attributes. In this paper, we will present Representative Multi-Label Bayesian Approach (RMLBA) to process the dataset with noisy attributes. RMLBA incorporates the affinity propagation (AP) approach and the Bayesian approach into the multi-label learning framework. Instead of considering all the attributes, RMLBA only focuses on a small subset of representative attributes which is detected by the AP. The experiments on image classification illustrate the RMLBA works well for the multi-label classification problems.

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Representative multi-label Bayesian approach for image classification

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