ABSTRACT
Multi-label classification is a generalization of conventional classification, where it is possible for a single data point to have multiple labels. Manual annotation of a multi-label data point requires a human oracle to consider the presence/absence of every possible class separately, which involves significant labor. Active learning techniques are effective in reducing human labeling effort to induce a classification model. When exposed to large quantities of unlabeled data, such algorithms automatically select the salient and representative instances for manual annotation. Further, to address the high redundancy in data such as image or video sequences as well as the availability of multiple labeling agents, there have been recent attempts towards a batch mode form of active learning, where a batch of data points is selected simultaneously from an unlabeled set. In this work, we propose a novel optimization based batch mode active learning strategy to minimize human labeling effort in multi-label classification problems. To the best of our knowledge, this is the first attempt to develop such a scheme primarily intended for the multi-label context. The proposed framework is computationally simple, easy to implement and can be suitably modified to perform batch mode active learning in other formulations, such as single-label classification or problems involving hierarchical label spaces. Our results corroborate the efficacy of the proposed algorithm and certify the potential of the framework in being used for real world applications.
- Y. Baram, R. El-Yaniv, and K. Luz. Online choice of active learning algorithms. JMLR, 5, 2004. Google ScholarDigital Library
- M. Boutell, J. Luo, X. Shen, and C. Brown. Learning multi-label scene classification. Pattern Recognition, 2004.Google Scholar
- K. Brinker. Incorporating diversity in active learning with support vector machines. ICML, 2003.Google Scholar
- K. Brinker. On active learning in multi-label classification. In SpringerLink, 2006.Google ScholarCross Ref
- D. Cohn, Z. Ghahramani, and M. Jordan. Active learning with statistical models. JAIR, 1996. Google ScholarDigital Library
- B. Gokberk, L. Akarun, and E. Alpaydin. Feature selection for pose invariant face recognition. In IEEE ICPR, 2002. Google ScholarDigital Library
- G.Tsoumakas and I. Katakis. Multi-label classification: An overview. In International Journal of Data Warehousing and Mining, 2007.Google Scholar
- Y. Guo and D. Schuurmans. Discriminative batch mode active learning. In NIPS, 2008.Google ScholarDigital Library
- S. Hoi, R. Jin, and M. R. Lyu. Large-scale text categorization by batch mode active learning. In International Conference on World Wide Web, 2006. Google ScholarDigital Library
- S. Hoi, R. Jin, J. Zhu, and M. Lyu. Semi-supervised SVM batch mode active learning for image retrieval. In IEEE CVPR, 2008.Google ScholarCross Ref
- S. Hoi, R. Jin, J. Zhu, and M. R. Lyu. Batch mode active learning and its application to medical image classification. In ICML, 2006. Google ScholarDigital Library
- X. Li, L. Wang, and E. Sung. Multi-label svm active learning for image classification. IEEE ICIP, 2004.Google Scholar
- Y. Liu and Z. Zhang. A fast algorithm for linearly constrained quadratic programming problems with lower and upper bounds. In International Conference on Multimedia and Information Technology, 2008. Google ScholarDigital Library
- M. Singh, E. Curran, and P. Cunningham. Active learning for multi-label image annotation. In Technical Report, University College Dublin, 2009.Google Scholar
- S. Tong and D. Koller. Support vector machine active learning with applications to text classification. JMLR, 2000. Google ScholarDigital Library
- X. Zhang, J. Cheng, C. Xu, H. Lu, and S. Ma. Multi-view multi-label active learning for image classification. In IEEE ICME, 2009. Google ScholarDigital Library
Index Terms
- Optimal batch selection for active learning in multi-label classification
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