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Multi-Objective Evolutionary Instance Selection for Regression Tasks
The purpose of instance selection is to reduce the data size while preserving as much useful information stored in the data as possible and detecting and removing the erroneous and redundant information. In this work, we analyze instance selection in regression tasks and apply the NSGA-II multi-obje...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512309/ https://www.ncbi.nlm.nih.gov/pubmed/33265835 http://dx.doi.org/10.3390/e20100746 |
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author | Kordos, Mirosław Łapa, Krystian |
author_facet | Kordos, Mirosław Łapa, Krystian |
author_sort | Kordos, Mirosław |
collection | PubMed |
description | The purpose of instance selection is to reduce the data size while preserving as much useful information stored in the data as possible and detecting and removing the erroneous and redundant information. In this work, we analyze instance selection in regression tasks and apply the NSGA-II multi-objective evolutionary algorithm to direct the search for the optimal subset of the training dataset and the k-NN algorithm for evaluating the solutions during the selection process. A key advantage of the method is obtaining a pool of solutions situated on the Pareto front, where each of them is the best for certain RMSE-compression balance. We discuss different parameters of the process and their influence on the results and put special efforts to reducing the computational complexity of our approach. The experimental evaluation proves that the proposed method achieves good performance in terms of minimization of prediction error and minimization of dataset size. |
format | Online Article Text |
id | pubmed-7512309 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75123092020-11-09 Multi-Objective Evolutionary Instance Selection for Regression Tasks Kordos, Mirosław Łapa, Krystian Entropy (Basel) Article The purpose of instance selection is to reduce the data size while preserving as much useful information stored in the data as possible and detecting and removing the erroneous and redundant information. In this work, we analyze instance selection in regression tasks and apply the NSGA-II multi-objective evolutionary algorithm to direct the search for the optimal subset of the training dataset and the k-NN algorithm for evaluating the solutions during the selection process. A key advantage of the method is obtaining a pool of solutions situated on the Pareto front, where each of them is the best for certain RMSE-compression balance. We discuss different parameters of the process and their influence on the results and put special efforts to reducing the computational complexity of our approach. The experimental evaluation proves that the proposed method achieves good performance in terms of minimization of prediction error and minimization of dataset size. MDPI 2018-09-29 /pmc/articles/PMC7512309/ /pubmed/33265835 http://dx.doi.org/10.3390/e20100746 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kordos, Mirosław Łapa, Krystian Multi-Objective Evolutionary Instance Selection for Regression Tasks |
title | Multi-Objective Evolutionary Instance Selection for Regression Tasks |
title_full | Multi-Objective Evolutionary Instance Selection for Regression Tasks |
title_fullStr | Multi-Objective Evolutionary Instance Selection for Regression Tasks |
title_full_unstemmed | Multi-Objective Evolutionary Instance Selection for Regression Tasks |
title_short | Multi-Objective Evolutionary Instance Selection for Regression Tasks |
title_sort | multi-objective evolutionary instance selection for regression tasks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512309/ https://www.ncbi.nlm.nih.gov/pubmed/33265835 http://dx.doi.org/10.3390/e20100746 |
work_keys_str_mv | AT kordosmirosław multiobjectiveevolutionaryinstanceselectionforregressiontasks AT łapakrystian multiobjectiveevolutionaryinstanceselectionforregressiontasks |