An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box

Several supervised machine learning algorithms focused on binary classification for solving daily problems can be found in the literature. The straight-line segment classifier stands out for its low complexity and competitiveness, compared to well-knownconventional classifiers. This binary classifie...

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Autores principales:	Rosario Medina-Rodríguez, César Beltrán-Castañón, Ronaldo Fumio Hashimoto
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	minimum bounding box straight-line segment classifier supervised learning Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/d056e445ec234d258a3aa4d1a2eccb2a
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spelling	oai:doaj.org-article:d056e445ec234d258a3aa4d1a2eccb2a2021-11-25T17:30:51ZAn Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box10.3390/e231115411099-4300https://doaj.org/article/d056e445ec234d258a3aa4d1a2eccb2a2021-11-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1541https://doaj.org/toc/1099-4300Several supervised machine learning algorithms focused on binary classification for solving daily problems can be found in the literature. The straight-line segment classifier stands out for its low complexity and competitiveness, compared to well-knownconventional classifiers. This binary classifier is based on distances between points and two labeled sets of straight-line segments. Its training phase consists of finding the placement of labeled straight-line segment extremities (and consequently, their lengths) which gives the minimum mean square error. However, during the training phase, the straight-line segment lengths can grow significantly, giving a negative impact on the classification rate. Therefore, this paper proposes an approach for adjusting the placements of labeled straight-line segment extremities to build reliable classifiers in a constrained search space (tuned by a scale factor parameter) in order to restrict their lengths. Ten artificial and eight datasets from the UCI Machine Learning Repository were used to prove that our approach shows promising results, compared to other classifiers. We conclude that this classifier can be used in industry for decision-making problems, due to the straightforward interpretation and classification rates.Rosario Medina-RodríguezCésar Beltrán-CastañónRonaldo Fumio HashimotoMDPI AGarticleminimum bounding boxstraight-line segment classifiersupervised learningScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1541, p 1541 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	minimum bounding box straight-line segment classifier supervised learning Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	minimum bounding box straight-line segment classifier supervised learning Science Q Astrophysics QB460-466 Physics QC1-999 Rosario Medina-Rodríguez César Beltrán-Castañón Ronaldo Fumio Hashimoto An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box
description	Several supervised machine learning algorithms focused on binary classification for solving daily problems can be found in the literature. The straight-line segment classifier stands out for its low complexity and competitiveness, compared to well-knownconventional classifiers. This binary classifier is based on distances between points and two labeled sets of straight-line segments. Its training phase consists of finding the placement of labeled straight-line segment extremities (and consequently, their lengths) which gives the minimum mean square error. However, during the training phase, the straight-line segment lengths can grow significantly, giving a negative impact on the classification rate. Therefore, this paper proposes an approach for adjusting the placements of labeled straight-line segment extremities to build reliable classifiers in a constrained search space (tuned by a scale factor parameter) in order to restrict their lengths. Ten artificial and eight datasets from the UCI Machine Learning Repository were used to prove that our approach shows promising results, compared to other classifiers. We conclude that this classifier can be used in industry for decision-making problems, due to the straightforward interpretation and classification rates.
format	article
author	Rosario Medina-Rodríguez César Beltrán-Castañón Ronaldo Fumio Hashimoto
author_facet	Rosario Medina-Rodríguez César Beltrán-Castañón Ronaldo Fumio Hashimoto
author_sort	Rosario Medina-Rodríguez
title	An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box
title_short	An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box
title_full	An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box
title_fullStr	An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box
title_full_unstemmed	An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box
title_sort	approach to growth delimitation of straight line segment classifiers based on a minimum bounding box
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d056e445ec234d258a3aa4d1a2eccb2a
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An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box

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