Vasisht, Deepak ; Damianou, Andreas ; Varma, Manik ; Kapoor, Ashish (2014) Active learning for sparse bayesian multilabel classification In: KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, August 2014, New York NY United States.
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Official URL: http://doi.org/10.1145/2623330.2623759
Related URL: http://dx.doi.org/10.1145/2623330.2623759
Abstract
We study the problem of active learning for multilabel classification. We focus on the real-world scenario where the average number of positive (relevant) labels per data point is small leading to positive label sparsity. Carrying out mutual information based near-optimal active learning in this setting is a challenging task since the computational complexity involved is exponential in the total number of labels. We propose a novel inference algorithm for the sparse Bayesian multilabel model of [17]. The benefit of this alternate inference scheme is that it enables a natural approximation of the mutual information objective. We prove that the approximation leads to an identical solution to the exact optimization problem but at a fraction of the optimization cost. This allows us to carry out efficient, non-myopic, and near-optimal active learning for sparse multilabel classification. Extensive experiments reveal the effectiveness of the method.
Item Type: | Conference or Workshop Item (Paper) |
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Source: | Copyright of this article belongs to Association for Computing Machinery. |
ID Code: | 119562 |
Deposited On: | 14 Jun 2021 10:22 |
Last Modified: | 14 Jun 2021 10:22 |
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