Prototype selection based on sequential search

Autor:	Jesús Ariel Carrasco-Ochoa, J. Fco. Martínez-Trinidad, José Arturo Olvera-López, Josef Kittler
Rok vydání:	2009
Předmět:	Training set Computer science business.industry computer.software_genre Machine learning Theoretical Computer Science Artificial Intelligence Computer Vision and Pattern Recognition Data mining Selection method Artificial intelligence business computer Selection (genetic algorithm) Linear search
Zdroj:	Intelligent Data Analysis. 13:599-631
ISSN:	1571-4128 1088-467X
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::fbe420dbd30529b7687b7c30a86026a0 https://doi.org/10.3233/ida-2009-0383 Zobrazit plný text záznamu Plný text ve formátu PDF