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A Belief Classification Approach Based on Artificial Immune Recognition System
Artificial Immune Recognition Systems (AIRS) are supervised classification methods inspired by the immune system metaphors. They enjoy a great popularity in the filed of machine learning by achieving good and competitive classification results. Nonetheless, while these approaches work properly under...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274700/ http://dx.doi.org/10.1007/978-3-030-50143-3_25 |
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author | Abdelkhalek, Rihab Elouedi, Zied |
author_facet | Abdelkhalek, Rihab Elouedi, Zied |
author_sort | Abdelkhalek, Rihab |
collection | PubMed |
description | Artificial Immune Recognition Systems (AIRS) are supervised classification methods inspired by the immune system metaphors. They enjoy a great popularity in the filed of machine learning by achieving good and competitive classification results. Nonetheless, while these approaches work properly under a certain framework, they present some weaknesses basically related to their inability to deal with uncertainty. This is considered as an important challenge in real-world classification problems. Furthermore, using traditional AIRS approaches, all memory cells are considered with the same importance during the classification process which may affect the final generated results. To tackle these issues, we propose in this paper a new AIRS approach under the belief function framework. Our approach tends to handle the uncertainty pervaded in the classification stage while taking into account the number of training antigens represented by each memory cell. The performance of the proposed evidential AIRS approach is validated on real-world data sets and compared to state of the art AIRS under certain and uncertain frameworks. |
format | Online Article Text |
id | pubmed-7274700 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72747002020-06-08 A Belief Classification Approach Based on Artificial Immune Recognition System Abdelkhalek, Rihab Elouedi, Zied Information Processing and Management of Uncertainty in Knowledge-Based Systems Article Artificial Immune Recognition Systems (AIRS) are supervised classification methods inspired by the immune system metaphors. They enjoy a great popularity in the filed of machine learning by achieving good and competitive classification results. Nonetheless, while these approaches work properly under a certain framework, they present some weaknesses basically related to their inability to deal with uncertainty. This is considered as an important challenge in real-world classification problems. Furthermore, using traditional AIRS approaches, all memory cells are considered with the same importance during the classification process which may affect the final generated results. To tackle these issues, we propose in this paper a new AIRS approach under the belief function framework. Our approach tends to handle the uncertainty pervaded in the classification stage while taking into account the number of training antigens represented by each memory cell. The performance of the proposed evidential AIRS approach is validated on real-world data sets and compared to state of the art AIRS under certain and uncertain frameworks. 2020-05-15 /pmc/articles/PMC7274700/ http://dx.doi.org/10.1007/978-3-030-50143-3_25 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 Abdelkhalek, Rihab Elouedi, Zied A Belief Classification Approach Based on Artificial Immune Recognition System |
title | A Belief Classification Approach Based on Artificial Immune Recognition System |
title_full | A Belief Classification Approach Based on Artificial Immune Recognition System |
title_fullStr | A Belief Classification Approach Based on Artificial Immune Recognition System |
title_full_unstemmed | A Belief Classification Approach Based on Artificial Immune Recognition System |
title_short | A Belief Classification Approach Based on Artificial Immune Recognition System |
title_sort | belief classification approach based on artificial immune recognition system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274700/ http://dx.doi.org/10.1007/978-3-030-50143-3_25 |
work_keys_str_mv | AT abdelkhalekrihab abeliefclassificationapproachbasedonartificialimmunerecognitionsystem AT elouedizied abeliefclassificationapproachbasedonartificialimmunerecognitionsystem AT abdelkhalekrihab beliefclassificationapproachbasedonartificialimmunerecognitionsystem AT elouedizied beliefclassificationapproachbasedonartificialimmunerecognitionsystem |