A Correction Method of a Base Classifier Applied to Imbalanced Data Classification

In this paper, the issue of tailoring the soft confusion matrix classifier to deal with imbalanced data is addressed. This is done by changing the definition of the soft neighbourhood of the classified object. The first approach is to change the neighbourhood to be more local by changing the Gaussia...

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Autores principales: Trajdos, Pawel, Kurzynski, Marek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303714/
http://dx.doi.org/10.1007/978-3-030-50423-6_7
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author Trajdos, Pawel
Kurzynski, Marek
author_facet Trajdos, Pawel
Kurzynski, Marek
author_sort Trajdos, Pawel
collection PubMed
description In this paper, the issue of tailoring the soft confusion matrix classifier to deal with imbalanced data is addressed. This is done by changing the definition of the soft neighbourhood of the classified object. The first approach is to change the neighbourhood to be more local by changing the Gaussian potential function approach to the nearest neighbour rule. The second one is to weight the instances that are included in the neighbourhood. The instances are weighted inversely proportional to the a priori class probability. The experimental results show that for one of the investigated base classifiers, the usage of the KNN neighbourhood significantly improves the classification results. What is more, the application of the weighting schema also offers a significant improvement.
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spelling pubmed-73037142020-06-19 A Correction Method of a Base Classifier Applied to Imbalanced Data Classification Trajdos, Pawel Kurzynski, Marek Computational Science – ICCS 2020 Article In this paper, the issue of tailoring the soft confusion matrix classifier to deal with imbalanced data is addressed. This is done by changing the definition of the soft neighbourhood of the classified object. The first approach is to change the neighbourhood to be more local by changing the Gaussian potential function approach to the nearest neighbour rule. The second one is to weight the instances that are included in the neighbourhood. The instances are weighted inversely proportional to the a priori class probability. The experimental results show that for one of the investigated base classifiers, the usage of the KNN neighbourhood significantly improves the classification results. What is more, the application of the weighting schema also offers a significant improvement. 2020-05-23 /pmc/articles/PMC7303714/ http://dx.doi.org/10.1007/978-3-030-50423-6_7 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Trajdos, Pawel
Kurzynski, Marek
A Correction Method of a Base Classifier Applied to Imbalanced Data Classification
title A Correction Method of a Base Classifier Applied to Imbalanced Data Classification
title_full A Correction Method of a Base Classifier Applied to Imbalanced Data Classification
title_fullStr A Correction Method of a Base Classifier Applied to Imbalanced Data Classification
title_full_unstemmed A Correction Method of a Base Classifier Applied to Imbalanced Data Classification
title_short A Correction Method of a Base Classifier Applied to Imbalanced Data Classification
title_sort correction method of a base classifier applied to imbalanced data classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303714/
http://dx.doi.org/10.1007/978-3-030-50423-6_7
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