Multi-objective learning of Relevance Vector Machine classifiers with multi-resolution kernels

• Published in: Pattern recognition Vol. 45; no. 9; pp. 3535 - 3543
• Main Authors: Clark, Andrew R.J., Everson, Richard M.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.09.2012; Elsevier
• Subjects: Applied sciences; Classification; Classifiers; Complexity; Cross-validation; Evolutionary algorithm; Exact sciences and technology; Information, signal and communications theory; Kernels; Mathematical analysis; Multi-resolution kernels; Optimization; Pattern recognition; Relevance Vector Machine; Signal and communications theory; Signal representation. Spectral analysis; Signal, noise; Telecommunications and information theory; Tradeoffs; Vectors (mathematics); Evolutionary algorithm; Cross-validation; Multi-resolution kernels; Classification; Relevance Vector Machine; Automatic classification; False positive; Pareto optimum; Evolutionary computation; Multiobjective programming; Cross validation; Vector method; Signal classification; Multiresolution analysis; Kernel method; Learning; Accuracy; Relevance criterion

The Relevance Vector Machine (RVM) is a sparse classifier in which complexity is controlled with the Automatic Relevance Determination prior. However, sparsity is dependent on kernel choice and severe over-fitting can occur. We describe multi-objective evolutionary algorithms (MOEAs) which optimise...