Neural Computing and Applications Robust Classification with Reject Option Using the Self-Organizing Map

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 developement 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 data sets. The obtained results suggest that the proposed SOM-based classifiers consistently outperform standard supervised classifiers. Ricardo Sousa? INEB – Instituto de Engenharia Biomédica, Universidade do Porto, Porto, Portugal, E-mail: contact@rsousa.org Ajalmar R. da Rocha Neto? Programa de Pós-Graduação em Engenharia de Telecomunicações, Instituto Federal do Ceará (IFCE), Fortaleza, Ceará, Brasil, E-mail: ajalmar@ifce.edu.br ?First two authors contributed equally to this manuscript. Jaime S. Cardoso INESC Porto, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal, E-mail: jaime.cardoso@inescporto.pt Guilherme A. Barreto Departamento de Engenharia de Teleinformática, Universidade Federal do Ceará (UFC), Fortaleza, Ceará, Brasil, E-mail: gbarreto@ufc.br Manuscript Click here to download Manuscript: article.tex Click here to view linked References 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 2 Ricardo Sousa? et al.