title: Ensemble pruning using spectral coefficients.
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.
authors.forename: Terry
authors.initials: T
authors.lastname: Windeatt
authors.forename: Cemre
authors.initials: C
authors.lastname: Zor