Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Review Help
Search
A Bayesian nonparametric model for multi-label learning
Xuan J., Lu J., Zhang G., Xu R., Luo X. Machine Learning106 (11):1787-1815,2017.Type:Article
Date Reviewed: May 14 2018

Existing generative models for multilabel learning require that the number of topics be fixed in advance. This paper proposes a Bayesian nonparametric model that does not have this requirement.

To make the exposition simpler, the authors use the example of author-document-word to illustrate the technique. In this application, the authors are the labels, documents are the instances, and the words of the documents are the features. The model uses a three-layer hierarchy in which the author level is modeled by a gamma process.

The details are nicely described with the aid of several diagrams that show the connections in the model. The algorithm, which is a Gibbs sampler that finds the parameters for the model, is detailed, and the authors give a theorem that justifies the use of the Gibbs sampler. Experimental results are given for three applications: the author-topic model; clinical free-text modeling, where the labels are the codes used for billing purposes; and protein classification, where the model could be used to predict the categories to which a protein belongs.

In each case, the authors’ model (MGNBP) is compared with other state-of-the-art models. The metrics used for comparison are Oneerror, Coverage, Rankingloss, and AveragePrecision.

In each case, MGNBP, while generally comparable to the other systems, is superior to the others for some of the metrics. For example, in the clinical case, MGNBP outranks the others on Oneerror and Rankingloss.

Reviewer:  J. P. E. Hodgson Review #: CR146030 (1807-0397)
Bookmark and Share
  Featured Reviewer  
 
Learning (I.2.6 )
 
Would you recommend this review?
yes
no
Other reviews under "Learning": Date
Learning in parallel networks: simulating learning in a probabilistic system
Hinton G. (ed) BYTE 10(4): 265-273, 1985. Type: Article
Nov 1 1985
Macro-operators: a weak method for learning
Korf R. Artificial Intelligence 26(1): 35-77, 1985. Type: Article
Feb 1 1986
Inferring (mal) rules from pupils’ protocols
Sleeman D.  Progress in artificial intelligence (, Orsay, France,391985. Type: Proceedings
Dec 1 1985
more...

E-Mail This Printer-Friendly
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright 1999-2024 ThinkLoud®
Terms of Use
| Privacy Policy