Robust classification with reject option using the self-organizing map

• Type: Article in International Scientific Journal
• Date: 2015

Authors

Ricardo Gamelas Sousa (Author) Other; Ajalmar R R Rocha Neto (Author) Other; Jaime S Cardoso (Author) FEUP; Guilherme A Barreto (Author) Other

Journal

Title: Neural Computing and Applications

Vol. 26

Pages: 1603-1619

ISSN: 0941-0643

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 9 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-00G-20A

DOI: 10.1007/s00521-015-1822-2

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17