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Calibrated simplex-mapping classification
We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the prediction of mere class labels, they also yield a confi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9844900/ https://www.ncbi.nlm.nih.gov/pubmed/36649243 http://dx.doi.org/10.1371/journal.pone.0279876 |
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author | Heese, Raoul Schmid, Jochen Walczak, Michał Bortz, Michael |
author_facet | Heese, Raoul Schmid, Jochen Walczak, Michał Bortz, Michael |
author_sort | Heese, Raoul |
collection | PubMed |
description | We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the prediction of mere class labels, they also yield a confidence level for each of their predictions. In essence, the training of our classifier proceeds in two steps. In a first step, the training data is represented in a latent space whose geometry is induced by a regular (n − 1)-dimensional simplex, n being the number of classes. We design this representation in such a way that it well reflects the feature space distances of the datapoints to their own- and foreign-class neighbors. In a second step, the latent space representation of the training data is extended to the whole feature space by fitting a regression model to the transformed data. With this latent-space representation, our calibrated classifier is readily defined. We rigorously establish its core theoretical properties and benchmark its prediction and calibration properties by means of various synthetic and real-world data sets from different application domains. |
format | Online Article Text |
id | pubmed-9844900 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-98449002023-01-18 Calibrated simplex-mapping classification Heese, Raoul Schmid, Jochen Walczak, Michał Bortz, Michael PLoS One Research Article We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the prediction of mere class labels, they also yield a confidence level for each of their predictions. In essence, the training of our classifier proceeds in two steps. In a first step, the training data is represented in a latent space whose geometry is induced by a regular (n − 1)-dimensional simplex, n being the number of classes. We design this representation in such a way that it well reflects the feature space distances of the datapoints to their own- and foreign-class neighbors. In a second step, the latent space representation of the training data is extended to the whole feature space by fitting a regression model to the transformed data. With this latent-space representation, our calibrated classifier is readily defined. We rigorously establish its core theoretical properties and benchmark its prediction and calibration properties by means of various synthetic and real-world data sets from different application domains. Public Library of Science 2023-01-17 /pmc/articles/PMC9844900/ /pubmed/36649243 http://dx.doi.org/10.1371/journal.pone.0279876 Text en © 2023 Heese et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Heese, Raoul Schmid, Jochen Walczak, Michał Bortz, Michael Calibrated simplex-mapping classification |
title | Calibrated simplex-mapping classification |
title_full | Calibrated simplex-mapping classification |
title_fullStr | Calibrated simplex-mapping classification |
title_full_unstemmed | Calibrated simplex-mapping classification |
title_short | Calibrated simplex-mapping classification |
title_sort | calibrated simplex-mapping classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9844900/ https://www.ncbi.nlm.nih.gov/pubmed/36649243 http://dx.doi.org/10.1371/journal.pone.0279876 |
work_keys_str_mv | AT heeseraoul calibratedsimplexmappingclassification AT schmidjochen calibratedsimplexmappingclassification AT walczakmichał calibratedsimplexmappingclassification AT bortzmichael calibratedsimplexmappingclassification |