Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
One-class svms for document classification
The Journal of Machine Learning Research
Automatically Labeling Video Data Using Multi-class Active Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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Confidence-Based Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
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CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
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Pattern Recognition
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SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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Pattern Recognition
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ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
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ACM Transactions on Information Systems (TOIS)
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ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Analysis of Perceptron-Based Active Learning
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IEEE Transactions on Knowledge and Data Engineering
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ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
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Journal of Artificial Intelligence Research
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
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ACM Transactions on Intelligent Systems and Technology (TIST)
Active Learning from Multiple Noisy Labelers with Varied Costs
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Active Learning with Human-Like Noisy Oracle
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Interactive Video Indexing With Statistical Active Learning
IEEE Transactions on Multimedia
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Active learning is to learn an accurate classifier within as few queried labels as possible. For practical applications, we propose a Certainty-Based Active Learning (CBAL) algorithm to solve the imbalanced data classification problem in active learning. Without being affected by irrelevant samples which might overwhelm the minority class, the importance of each unlabeled sample is carefully measured within an explored neighborhood. For handling the agnostic case, IWAL-ERM is integrated into our approach without costs. Thus our CBAL is designed to determine the query probability within an explored neighborhood for each unlabeled sample. The potential neighborhood is incrementally explored, and there is no need to define the neighborhood size in advance. In our theoretical analysis, it is presented that CBAL has a polynomial label query improvement over passive learning. And the experimental results on synthetic and real-world datasets show that, CBAL has the ability of identifying informative samples and dealing with the imbalanced data classification problem in active learning.