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

Autor(es)

Medina-Rodriguez, Rosario ^[1] ; Beltran-Castanon, Cesar ^[1] ; Hashimoto, Ronaldo Fumio ^[2]

Afiliação do(s) autor(es)

^[1] Pontificia Univ Catolica Peru, Escuela Posgrad, Dept Ingn, Av Univ 1801, Lima 15088 - Peru

^[2] Univ Sao Paulo, Inst Matemat & Estat, Dept Ciencia Computacao, Rua Matao 1010, BR-05508900 Sao Paulo, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Entropy; v. 23, n. 11 NOV 2021.
• Citações Web of Science: 0

Resumo

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. (AU)

• Processo FAPESP: 15/22308-2 - Representações intermediárias em Ciência Computacional para descoberta de conhecimento
• Beneficiário: Roberto Marcondes Cesar Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático