caa a22 4500 606159266 CHVBK 20210128100623.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00521-015-1822-2 doi (NATIONALLICENCE)springer-10.1007/s00521-015-1822-2 Robust classification with reject option using the self-organizing map [Elektronische Daten] [Ricardo Gamelas Sousa, Ajalmar Rocha Neto, Jaime Cardoso, Guilherme Barreto] Reject option is a technique used to improve classifier's reliability in decision support systems. It consists in withholding the automatic classification of an item, if the decision is considered not sufficiently reliable. The rejected item is then handled by a different classifier or by a human expert. The vast majority of the works on this issue has been concerned with the development of reject option mechanisms to be used by supervised learning architectures (e.g., MLP, LVQ or SVM). In this paper, however, we aim at proposing alternatives to this view, which are based on the self-organizing map (SOM), originally an unsupervised learning scheme, but that has also been successfully used in the design of prototype-based classifiers. The basic hypothesis we defend is that it is possible to design SOM-based classifiers endowed with reject option mechanisms whose performances are comparable to or better than those achieved by standard supervised classifiers. For this purpose, we carried out a comprehensively evaluation of the proposed SOM-based classifiers on two synthetic and three real-world datasets. The obtained results suggest that the proposed SOM-based classifiers consistently outperform standard supervised classifiers. The Natural Computing Applications Forum, 2015 Self-organizing maps nationallicence Reject option nationallicence Robust classification nationallicence Prototype-based classifiers nationallicence Neuron labeling nationallicence Gamelas Sousa Ricardo Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal aut Rocha Neto Ajalmar Departamento de Telemática, Instituto Federal do Ceará (IFCE), Fortaleza, Ceará, Brasil aut Cardoso Jaime INESC TEC and Faculdade de Engenharia da Universidade do Porto, Porto, Portugal aut Barreto Guilherme Departamento de Engenharia de Teleinformática, Universidade Federal do Ceará (UFC), Fortaleza, Ceará, Brasil aut Neural Computing and Applications Springer London 26/7(2015-10-01), 1603-1619 0941-0643 26:7<1603 2015 26 521 https://doi.org/10.1007/s00521-015-1822-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1822-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Gamelas Sousa Ricardo Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal aut NATIONALLICENCE 700 1- Rocha Neto Ajalmar Departamento de Telemática, Instituto Federal do Ceará (IFCE), Fortaleza, Ceará, Brasil aut NATIONALLICENCE 700 1- Cardoso Jaime INESC TEC and Faculdade de Engenharia da Universidade do Porto, Porto, Portugal aut NATIONALLICENCE 700 1- Barreto Guilherme Departamento de Engenharia de Teleinformática, Universidade Federal do Ceará (UFC), Fortaleza, Ceará, Brasil aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/7(2015-10-01), 1603-1619 0941-0643 26:7<1603 2015 26 521