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Classifying with confidence using Bayes rule and kernel density estimation

Fearn, T; Pérez Marín, D; Garrido Varo, A; Guerrero Ginel, JE; (2019) Classifying with confidence using Bayes rule and kernel density estimation. Chemometrics and Intelligent Laboratory Systems , 189 pp. 81-87. 10.1016/j.chemolab.2019.04.0004.

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Abstract

An example in which near infrared spectroscopic data are used to classify animal feed ingredients is used to make the case for the value of probabilistic approaches to classification problems. The accuracy of probabilities given by linear and quadratic discriminant analysis and by a more flexible kernel density approach are examined, and the effect on these probabilities of the use of different tuning criteria is explored. The example involves the classification of multiple particles in a sample, and detailed probability calculations bearing on the inference for both the sample and its parent population are presented.

Type: Article
Title: Classifying with confidence using Bayes rule and kernel density estimation
DOI: 10.1016/j.chemolab.2019.04.0004
Publisher version: https://doi.org/10.1016/j.chemolab.2019.04.0004
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Classification, Probability, Kernel density estimation, Near infrared spectroscopy
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10075830
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