NDLI: A Generative Probabilistic Model for Multi-label Classification

Content Provider	IEEE Xplore Digital Library
Author	Hongning Wang Minlie Huang Xiaoyan Zhu
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing (Hongning Wang; Minlie Huang; Xiaoyan Zhu)
Abstract	Traditional discriminative classification method makes little attempt to reveal the probabilistic structure and the correlation within both input and output spaces. In the scenario of multi-label classification, most of the classifiers simply assume the predefined classes are independently distributed, which would definitely hinder the classification performance when there are intrinsic correlations between the classes. In this article, we propose a generative probabilistic model, the Correlated Labeling Model (CoL Model), to formulate the correlation between different classes. The CoL model is presented to capture the correlation between classes and the underlying structures via the latent random variables in a supervised manner. We develop a variational procedure to approximate the posterior distribution and employ the EM algorithm for the empirical Bayes parameter estimation. In our evaluations, the proposed model achieved promising results on various data sets.
Starting Page	628
Ending Page	637
File Size	488130
Page Count	10
File Format	PDF
ISBN	9780769535029
ISSN	15504786
DOI	10.1109/ICDM.2008.86
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-15
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Space technology Laboratories Text categorization Data mining Intelligent systems Intelligent structures Information science Computer science Labeling Random variables text classification multi-label classification generative model variational inference
Content Type	Text
Resource Type	Article

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