Multi-objective learning of Relevance Vector Machine classifiers with multi-resolution kernels
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...
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Published in | Pattern recognition Vol. 45; no. 9; pp. 3535 - 3543 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Kidlington
Elsevier Ltd
01.09.2012
Elsevier |
Subjects | |
Online Access | Get full text |
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Summary: | 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 RVMs allowing selection of the best operating true and false positive rates and complexity from the Pareto set of optimal trade-offs. We introduce several cross-validation methods for use during evolutionary optimisation. Comparisons on benchmark datasets using multi-resolution kernels show that the MOEAs can locate markedly sparser RVMs than the standard, with comparable accuracies.
► The RVM is a sparse classifier which uses ARD to control complexity. ► Kernel choice can lead to severe overfitting. ► Multi-objective evolutionary algorithms provide Pareto set of optimal trade-offs. ► Objectives are true positive rate, false positive rate and complexity. ► K-fold cross-validation during optimisation effectively controls over-fitting. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0031-3203 1873-5142 |
DOI: | 10.1016/j.patcog.2012.02.025 |