Active Learning without Knowing Individual Instance Labels: A Pairwise Label Homogeneity Query Approach
Issue No. 04 - April (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.165
Yifan Fu , Sch. of Comput. & Math., Charles Sturt Univ., Bathurst, NSW, Australia
Bin Li , Center for Quantum Comput. & Intell. Syst. (QCIS), Univ. of Technol., Sydney (UTS), Sydney, NSW, Australia
Xingquan Zhu , Dept. of Comput. & Electr. Eng. & Comput. Sci., Florida Atlantic Univ., Boca Raton, FL, USA
Chengqi Zhang , Center for Quantum Comput. & Intell. Syst. (QCIS), Univ. of Technol., Sydney (UTS), Sydney, NSW, Australia
Traditional active learning methods require the labeler to provide a class label for each queried instance. The labelers are normally highly skilled domain experts to ensure the correctness of the provided labels, which in turn results in expensive labeling cost. To reduce labeling cost, an alternative solution is to allow nonexpert labelers to carry out the labeling task without explicitly telling the class label of each queried instance. In this paper, we propose a new active learning paradigm, in which a nonexpert labeler is only asked “whether a pair of instances belong to the same class”, namely, a pairwise label homogeneity. Under such circumstances, our active learning goal is twofold: (1) decide which pair of instances should be selected for query, and (2) how to make use of the pairwise homogeneity information to improve the active learner. To achieve the goal, we propose a “Pairwise Query on Max-flow Paths” strategy to query pairwise label homogeneity from a nonexpert labeler, whose query results are further used to dynamically update a Min-cut model (to differentiate instances in different classes). In addition, a “Confidence-based Data Selection” measure is used to evaluate data utility based on the Min-cut model's prediction results. The selected instances, with inferred class labels, are included into the labeled set to form a closed-loop active learning process. Experimental results and comparisons with state-of-the-art methods demonstrate that our new active learning paradigm can result in good performance with nonexpert labelers.
Labeling, Data models, Predictive models, Noise measurement, Prediction algorithms, Learning systems, Accuracy
Yifan Fu, Bin Li, Xingquan Zhu and Chengqi Zhang, "Active Learning without Knowing Individual Instance Labels: A Pairwise Label Homogeneity Query Approach," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 4, pp. 808-822, 2014.