Attribute and label distribution driven multi-label active learning |
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Authors: | Wang Min Feng Tingting Shan Zhaohui Min Fan |
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Affiliation: | 1.Electrical Engineering and Information, Southwest Petroleum University, Chengdu, 610500, China ;2.China National Petroleum Corporation Xinjiang Oilfield Branch, Karamay, 834000, China ;3.Computer Science, Southwest Petroleum University, Chengdu, 610500, China ; |
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Abstract: | In multi-label learning, each instance is simultaneously associated with multiple class labels. A large number of labels in an application exacerbates the problem of label scarcity. An interesting issue concerns how to query as few labels as possible while obtaining satisfactory classification accuracy. For this purpose, we propose the attribute and label distribution driven multi-label active learning (MCAL) algorithm. MCAL considers the characteristics of both attributes and labels to enable the selection of critical instances based on different measures. Representativeness is measured by the probability density function obtained by non-parametric estimation, while informativeness is measured by the bilateral softmax predicted entropy. Diversity is measured by the distance metric among instances, and richness is measured by the number of softmax predicted labels. We describe experiments performed on eight benchmark datasets and eleven real Yahoo webpage datasets. The results verify the effectiveness of MCAL and its superiority over state-of-the-art multi-label algorithms and multi-label active learning algorithms. |
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