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FASTENER Feature Selection for Inference from Earth Observation Data
In this paper, a novel feature selection algorithm for inference from high-dimensional data (FASTENER) is presented. With its multi-objective approach, the algorithm tries to maximize the accuracy of a machine learning algorithm with as few features as possible. The algorithm exploits entropy-based...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712113/ https://www.ncbi.nlm.nih.gov/pubmed/33286966 http://dx.doi.org/10.3390/e22111198 |
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author | Koprivec, Filip Kenda, Klemen Šircelj, Beno |
author_facet | Koprivec, Filip Kenda, Klemen Šircelj, Beno |
author_sort | Koprivec, Filip |
collection | PubMed |
description | In this paper, a novel feature selection algorithm for inference from high-dimensional data (FASTENER) is presented. With its multi-objective approach, the algorithm tries to maximize the accuracy of a machine learning algorithm with as few features as possible. The algorithm exploits entropy-based measures, such as mutual information in the crossover phase of the iterative genetic approach. FASTENER converges to a (near) optimal subset of features faster than other multi-objective wrapper methods, such as POSS, DT-forward and FS-SDS, and achieves better classification accuracy than similarity and information theory-based methods currently utilized in earth observation scenarios. The approach was primarily evaluated using the earth observation data set for land-cover classification from ESA’s Sentinel-2 mission, the digital elevation model and the ground truth data of the Land Parcel Identification System from Slovenia. For land cover classification, the algorithm gives state-of-the-art results. Additionally, FASTENER was tested on open feature selection data sets and compared to the state-of-the-art methods. With fewer model evaluations, the algorithm yields comparable results to DT-forward and is superior to FS-SDS. FASTENER can be used in any supervised machine learning scenario. |
format | Online Article Text |
id | pubmed-7712113 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77121132021-02-24 FASTENER Feature Selection for Inference from Earth Observation Data Koprivec, Filip Kenda, Klemen Šircelj, Beno Entropy (Basel) Article In this paper, a novel feature selection algorithm for inference from high-dimensional data (FASTENER) is presented. With its multi-objective approach, the algorithm tries to maximize the accuracy of a machine learning algorithm with as few features as possible. The algorithm exploits entropy-based measures, such as mutual information in the crossover phase of the iterative genetic approach. FASTENER converges to a (near) optimal subset of features faster than other multi-objective wrapper methods, such as POSS, DT-forward and FS-SDS, and achieves better classification accuracy than similarity and information theory-based methods currently utilized in earth observation scenarios. The approach was primarily evaluated using the earth observation data set for land-cover classification from ESA’s Sentinel-2 mission, the digital elevation model and the ground truth data of the Land Parcel Identification System from Slovenia. For land cover classification, the algorithm gives state-of-the-art results. Additionally, FASTENER was tested on open feature selection data sets and compared to the state-of-the-art methods. With fewer model evaluations, the algorithm yields comparable results to DT-forward and is superior to FS-SDS. FASTENER can be used in any supervised machine learning scenario. MDPI 2020-10-23 /pmc/articles/PMC7712113/ /pubmed/33286966 http://dx.doi.org/10.3390/e22111198 Text en © 2020 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 Koprivec, Filip Kenda, Klemen Šircelj, Beno FASTENER Feature Selection for Inference from Earth Observation Data |
title | FASTENER Feature Selection for Inference from Earth Observation Data |
title_full | FASTENER Feature Selection for Inference from Earth Observation Data |
title_fullStr | FASTENER Feature Selection for Inference from Earth Observation Data |
title_full_unstemmed | FASTENER Feature Selection for Inference from Earth Observation Data |
title_short | FASTENER Feature Selection for Inference from Earth Observation Data |
title_sort | fastener feature selection for inference from earth observation data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712113/ https://www.ncbi.nlm.nih.gov/pubmed/33286966 http://dx.doi.org/10.3390/e22111198 |
work_keys_str_mv | AT koprivecfilip fastenerfeatureselectionforinferencefromearthobservationdata AT kendaklemen fastenerfeatureselectionforinferencefromearthobservationdata AT sirceljbeno fastenerfeatureselectionforinferencefromearthobservationdata |