Using weighted dynamic classifier selection methods in ensembles with different levels of diversity

Autor:	Diogo Fagundes, João C. Xavier Junior, Marjory C. C. Abreu, Anne M. P. Canuto
Rok vydání:	2006
Předmět:	Computer science business.industry Confidence measures Margin classifier Pattern recognition Selection method Artificial intelligence Machine learning computer.software_genre business computer Classifier (UML)
Zdroj:	International Journal of Hybrid Intelligent Systems. 3:147-158
ISSN:	1875-8819 1448-5869
DOI:	10.3233/his-2006-3303
Popis:	There are two main approaches to combine the output of classifiers within a multi-classifier system (MCS), which are: combination-based and selection-based methods. In selection-based methods, only one classifier is needed to correctly classify the input pattern. The choice of a classifier is typically based on the certainty of the current decision. The use of weights can be very useful for the final decision of a selection-based MCS since it can provide a confidence degree for each classifier. This paper presents the use of two confidence measures applied in three selection-based methods. The main aim of this paper is to analyze the benefits of using weights in the main selection-based methods and which confidence measure is more suitable to be used.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::14f9d7cec720087dc8516c94cc4cad56 https://doi.org/10.3233/his-2006-3303 Zobrazit plný text záznamu