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Topic Model Based Multi-Label Classification

Padmanabhan, Divya and Bhat, Satyanath and Shevade, Shirish and Narahari, Y (2016) Topic Model Based Multi-Label Classification. In: 28th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), NOV 06-08, 2016, San Jose, CA, pp. 996-1003.

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Official URL: http://dx.doi.org/10.1109/ICTAI.2016.0154

Abstract

Multi-label classification is a common supervised machine learning problem where each instance is associated with multiple classes. The key challenge in this problem is learning the correlations between the classes. An additional challenge arises when the labels of the training instances are provided by noisy, heterogeneous crowd-workers with unknown qualities. We first assume labels from a perfect source and propose a novel topic model (ML-PA-LDA) where the classes that are present as well as the classes absent generate the latent topics and hence the words. Extensive experimentation on real world datasets reveals the superior performance of the proposed model. We then non-trivially extend our topic model to the scenario where the labels are provided by noisy crowd-workers and refer to this model as ML-PA-LDA-C. With experiments on simulated crowd, the proposed model learns the qualities of the annotators well, even with minimal training data.

Item Type: Conference Proceedings
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Id for Latest eprints
Date Deposited: 22 Jul 2017 07:09
Last Modified: 05 Nov 2018 09:41
URI: http://eprints.iisc.ac.in/id/eprint/57483

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