Representative multi-label Bayesian approach for image classification

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-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 languageEnglish
Title of host publicationProceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Pages1395-1399
Number of pages5
Volume4
DOIs
Publication statusPublished - 31 Dec 2012
Event2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 - Xian, Shaanxi, China
Duration: 15 Jul 201217 Jul 2012

Conference

Conference2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Country/TerritoryChina
CityXian, Shaanxi
Period15/07/1217/07/12

ASJC Scopus subject areas

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

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