Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/1363
Title: Gaussian conditional random fields for classification
Authors: Petrović, Andrija
Nikolić, Mladen 
Jovanović, Miloš
Delibašić, Boris
Affiliations: Informatics and Computer Science 
Keywords: Discriminative graph-based model;Empirical Bayes;Gaussian conditional random fields;Local variational approximation;Structured classification
Issue Date: 1-Feb-2023
Rank: M21a
Publisher: Pergamon press
Journal: Expert Systems with Applications
Abstract: 
Gaussian conditional random fields (GCRF) are a well-known structured model for continuous outputs that uses multiple unstructured predictors to form its features and at the same time exploits dependence structure among outputs, which is provided by a similarity measure. In this paper, a Gaussian conditional random field model for structured binary classification (GCRFBC) is proposed. The model is applicable to classification problems with undirected graphs, intractable for standard classification CRFs. The model representation of GCRFBC is extended by latent variables which yield some appealing properties. Thanks to the GCRF latent structure, the model becomes tractable, efficient and open to improvements previously applied to GCRF regression models. In addition, the model allows for reduction of noise, that might appear if structures were defined directly between discrete outputs. Two different forms of the algorithm are presented: GCRFBCb (GCRGBC — Bayesian) and GCRFBCnb (GCRFBC — non-Bayesian). The extended method of local variational approximation of sigmoid function is used for solving empirical Bayes in Bayesian GCRFBCb variant, whereas MAP value of latent variables is the basis for learning and inference in the GCRFBCnb variant. The inference in GCRFBCb is solved by Newton–Cotes formulas for one-dimensional integration. Both models are evaluated on synthetic data and real-world data. We show that both models achieve better prediction performance than unstructured predictors. Furthermore, computational and memory complexity is evaluated. Advantages and disadvantages of the proposed GCRFBCb and GCRFBCnb are discussed in detail.
URI: https://research.matf.bg.ac.rs/handle/123456789/1363
ISSN: 09574174
DOI: 10.1016/j.eswa.2022.118728
Rights: Attribution-NonCommercial-NoDerivs 3.0 United States
Appears in Collections:Research outputs

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