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Information 2017, 8(2), 52; doi:10.3390/info8020052

Multi-Label Classification from Multiple Noisy Sources Using Topic Models

Department of Computer Science and Automation, Indian Institute of Science, Bangalore-560012, India
This paper is an extended version of our paper published in TMNZ 2016 and IEEE ICTAI 2016.
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Academic Editor: Willy Susilo
Received: 24 January 2017 / Revised: 24 April 2017 / Accepted: 27 April 2017 / Published: 5 May 2017
(This article belongs to the Special Issue Text Mining Applications and Theory)
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Abstract

Multi-label classification is a well-known supervised machine learning setting where each instance is associated with multiple classes. Examples include annotation of images with multiple labels, assigning multiple tags for a web page, etc. Since several labels can be assigned to a single instance, one of the key challenges in this problem is to learn the correlations between the classes. Our first contribution assumes labels from a perfect source. Towards this, we propose a novel topic model (ML-PA-LDA). The distinguishing feature in our model is that classes that are present as well as the classes that are absent generate the latent topics and hence the words. Extensive experimentation on real world datasets reveals the superior performance of the proposed model. A natural source for procuring the training dataset is through mining user-generated content or directly through users in a crowdsourcing platform. In this more practical scenario of crowdsourcing, an additional challenge arises as the labels of the training instances are provided by noisy, heterogeneous crowd-workers with unknown qualities. With this motivation, we further augment our topic model to the scenario where the labels are provided by multiple noisy sources and refer to this model as ML-PA-LDA-MNS. With experiments on simulated noisy annotators, the proposed model learns the qualities of the annotators well, even with minimal training data. View Full-Text
Keywords: multi-label classification; topic models; multiple sources; variational inference multi-label classification; topic models; multiple sources; variational inference
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Padmanabhan, D.; Bhat, S.; Shevade, S.; Narahari, Y. Multi-Label Classification from Multiple Noisy Sources Using Topic Models. Information 2017, 8, 52.

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