Prototype selection based on sequential search

Article type: Research Article

Authors: Olvera-López, J.A.^{a; *} | Martínez-Trinidad, J. Fco.^a | Carrasco-Ochoa, J.A.^a | Kittler, J.^b

Affiliations: [a] Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Sta. María Tonantzintla, Puebla, CP: 72840, Mexico | [b] Center for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: In this paper, we propose and explore the use of the sequential search for solving the prototype selection problem since this kind of search has shown good performance for solving selection problems. We propose three prototype selection methods based on sequential search. The main goal of our methods is to reduce the training data without losing too much classification accuracy. Experiments and results are reported showing the effectiveness of the proposed methods and comparing their performance against other prototype selection methods.

Keywords: Prototype selection, quality training set, supervised classification, sequential search

DOI: 10.3233/IDA-2009-0383

Journal: Intelligent Data Analysis, vol. 13, no. 4, pp. 599-631, 2009