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

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:31:48Z
Fecha de publicación2021-01-01
ResumenSeveral 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.es_ES
Doihttps://doi.org/10.3390/e23111541es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/3520
Idiomaenes_ES
EditorialMDPI AGes_ES
RelaciónEntropyes_ES
URL relacionadohttps://doi.org/10.3390/e23111541es_ES
DerechosAcceso restringido / Suscripción (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_16eces_ES
FuenteEntropy
TítuloAn Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Boxes_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorMedina-Rodríguez, Rosario
AutorBeltrán-Castañón, César
AutorHashimoto, Ronaldo Fumio
AutorMedina-Rodríguez, Rosarioes_ES
AutorBeltrán-Castañón, Césares_ES
AutorHashimoto, Ronaldo Fumioes_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número11es_ES
Rango de páginas1541es_ES
URL relacionadahttps://doi.org/10.3390/e23111541
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen23es_ES

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