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IEEE Transactions on Pattern Analysis and Machine Intelligence
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An alternative method of stochastic discrimination with applications to pattern recognition
An alternative method of stochastic discrimination with applications to pattern recognition
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
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MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
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MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
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We studied several measures of the complexity of classification problems and related them to the comparative advantages of two methods for creating multiple classifier systems. Using decision trees as prototypical classifiers and bootstrapping and subspace projection as classifier generation methods, we studied a collection of 437 two-class problems from public databases. We observed strong correlations between classifier accuracies, a measure of class boundary length, and a measure of class manifold thickness. Also, the bootstrapping method appears to be better when subsamples yield more variable boundary measures and the subspace method excels when many features contribute evenly to the discrimination.