Title :
Ensemble Pruning Using Spectral Coefficients
Author :
Windeatt, T. ; Zor, C.
Author_Institution :
Centre for Vision Speech & Signal Process., Univ. of Surrey, Guildford, UK
Abstract :
Ensemble pruning aims to increase efficiency by reducing the number of base classifiers, without sacrificing and preferably enhancing performance. In this brief, a novel pruning paradigm is proposed. Two class supervised learning problems are pruned using a combination of first- and second-order Walsh coefficients. A comparison is made with other ordered aggregation pruning methods, using multilayer perceptron base classifiers. The Walsh pruning method is analyzed with the help of a model that shows the relationship between second-order coefficients and added classification error with respect to Bayes error.
Keywords :
Bayes methods; Walsh functions; multilayer perceptrons; pattern classification; Bayes error; classification error; ensemble pruning; first-order Walsh coefficients; multilayer perceptron base classifier; second-order Walsh coefficients; spectral coefficient; supervised learning problem; Correlation; Error analysis; Indexes; Learning systems; Polynomials; Training; Vectors; Classification; ensemble pruning; pattern analysis;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2013.2239659
