Improving a dynamic ensemble selection method based on oracle information. (1st January 2012)

• Record Type: Journal Article
• Authors: Vriesmann, Leila Maria; de Souza Britto, Alceu; de Oliveira, Luiz Eduardo Soares; Sabourin, Robert; Ko, Albert Houng-Ren
• Is Part Of: International journal of innovative computing and applications. Volume 4:Number 3/4(2012)
• Page Start: 184
• Page End: 200
• Publication Date: 2012-01-01
• Subjects: dynamic ensemble selection -- oracle -- K-nearest-oracles -- KNORA; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

This work evaluates some strategies to approximate the performance of a dynamic ensemble selection method to the oracle performance of its pool of weak classifiers. For this purpose, we evaluated different distance metrics in the K-nearest-oracles (KNORA) method, the use of statistics related to the class accuracy of each classifier in the pool and some additional information calculated by using a clustering process in the validation dataset. Moreover, different strategies are also evaluated to combine the results of the KNORA dynamic ensemble selection method with the results of its built-in K-nearest neighbour (KNN) used to define the neighbourhood of a test pattern during the ensemble creation. A strong experimental protocol based on more than 60, 000 samples of handwriting digits extracted from NIST-SD19 was used to evaluate each strategy. The experiments have shown that the fusion of the KNORA results with the results of its built-in KNN is a very promising strategy.