Bayesian Analysis

Improving classification when a class hierarchy is available using a hierarchy-based prior

Radford M. Neal and Babak Shahbaba

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Abstract

We introduce a new method for building classification models when we have prior knowledge of how the classes can be arranged in a hierarchy, based on how easily they can be distinguished. The new method uses a Bayesian form of the multinomial logit (MNL, a.k.a. "softmax") model, with a prior that introduces correlations between the parameters for classes that are nearby in the tree. We compare the performance on simulated data of the new method, the ordinary MNL model, and a model that uses the hierarchy in a different way. We also test the new method on page layout analysis and document classification problems, and find that it performs better than the other methods.

Article information

Source
Bayesian Anal. Volume 2, Number 1 (2007), 221-237.

Dates
First available in Project Euclid: 22 June 2012

Permanent link to this document
http://projecteuclid.org/euclid.ba/1340390069

Digital Object Identifier
doi:10.1214/07-BA209

Mathematical Reviews number (MathSciNet)
MR2289929

Subjects
Primary: Database Expansion Item

Keywords
Hierarchical Classification Bayesian Models Multinomial Logistic Regression Page Layout Analysis Document Classification

Citation

Shahbaba, Babak; Neal, Radford M. Improving classification when a class hierarchy is available using a hierarchy-based prior. Bayesian Anal. 2 (2007), no. 1, 221--237. doi:10.1214/07-BA209. http://projecteuclid.org/euclid.ba/1340390069.


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