Circular Bayesian classifiers using wrapped Cauchy distributions

Leguey Vitoriano, Ignacio and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María (2019). Circular Bayesian classifiers using wrapped Cauchy distributions. "Data & Knowledge Engineering", v. 122 ; pp. 101-115. ISSN 0169-023X. https://doi.org/10.1016/j.datak.2019.05.005.

Description

Title: Circular Bayesian classifiers using wrapped Cauchy distributions
Author/s:
  • Leguey Vitoriano, Ignacio
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
Item Type: Article
Título de Revista/Publicación: Data & Knowledge Engineering
Date: July 2019
ISSN: 0169-023X
Volume: 122
Subjects:
Freetext Keywords: Data mining; Classification; Circular statistics; Wrapped Cauchy distribution; Bayesian networks; Cortical layer
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Capturing the dependences among circular variables within supervised classification models is a challenging task. In this paper, we propose four different supervised Bayesian classification algorithms where the predictor variables follow all circular wrapped Cauchy distributions. For this purpose, we introduce four wrapped Cauchy classifiers. The bivariate wrapped Cauchy distribution is the only bivariate circular distribution whose marginals and conditionals are also wrapped Cauchy distributions, a property that makes it possible to define these models easily. Furthermore, the wrapped Cauchy tree-augmented naive Bayes classifier requires the definition of a conditional circular mutual information measure between variables that follow wrapped Cauchy distributions. Synthetic data is used to illustrate, compare and evaluate the classification algorithms (including a comparison with the Gaussian TAN classifier, decision tree, random forest, multinomial logistic regression, support vector machine and simple neural network), leading to satisfactory predictive results. We also use a real neuromorphological dataset obtained from juvenile rat somatosensory cortex cells, where we measure the bifurcation angles of the dendritic basal arbors.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTIN2016-796842-PUnspecifiedUnspecifiedUnspecified
Government of SpainC080020-09UnspecifiedUnspecifiedCajal Blue Brain
Horizon 2020785907HBP SGA2École Polytechnique Fédérale de LausanneHuman Brain Project Specific Grant Agreement 2
Government of SpainFPU13/01941UnspecifiedUniversidad Politécnica de MadridUnspecified

More information

Item ID: 63569
DC Identifier: http://oa.upm.es/63569/
OAI Identifier: oai:oa.upm.es:63569
DOI: 10.1016/j.datak.2019.05.005
Official URL: https://www.sciencedirect.com/science/article/pii/S0169023X17304937
Deposited by: Memoria Investigacion
Deposited on: 06 Nov 2020 09:40
Last Modified: 06 Nov 2020 09:40
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