Optimizing nearest neighbour in random subspaces using a multi-objective genetic algorithm

In this work, the authors have evaluated almost 20 millions ensembles of classifiers generated by several methods. Trying to optimize those ensembles based on the nearest neighbours and the random subspaces paradigms, we found that the use of a diversity metric called "ambiguity" had no be...

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Bibliographic Details
Main Authors: Tremblay, G., Sabourin, R., Maupin, P.
Format: Conference Proceeding
Language:English
Subjects:
Genetic algorithms
NIST
Optimization methods
Pattern recognition
Performance loss
Prototypes
Research and development
Stochastic processes
Voting
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Online Access:Request full text
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Description
Summary:In this work, the authors have evaluated almost 20 millions ensembles of classifiers generated by several methods. Trying to optimize those ensembles based on the nearest neighbours and the random subspaces paradigms, we found that the use of a diversity metric called "ambiguity" had no better positive impact than plain stochastic search.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2004.1334060