Multi-Label Classification from Multiple Noisy Sources Using Topic Models†
AbstractMulti-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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Padmanabhan, D.; Bhat, S.; Shevade, S.; Narahari, Y. Multi-Label Classification from Multiple Noisy Sources Using Topic Models. Information 2017, 8, 52.
Padmanabhan D, Bhat S, Shevade S, Narahari Y. Multi-Label Classification from Multiple Noisy Sources Using Topic Models. Information. 2017; 8(2):52.Chicago/Turabian Style
Padmanabhan, Divya; Bhat, Satyanath; Shevade, Shirish; Narahari, Y. 2017. "Multi-Label Classification from Multiple Noisy Sources Using Topic Models." Information 8, no. 2: 52.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.