Using genetic algorithms to select and create features for pattern classification
E.I. Chang,Richard P. Lippmann,D.W. Tong +2 more
- pp 747-752
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TLDR
On a difficult artificial machine-vision task, genetic algorithms were able to create new features (polynomial functions of the original features) which dramatically reduced classification error rates.Abstract:
Genetic algorithms were used for feature selection and creation in two pattern-classification problems. On a machine-version inspection task, it was found that genetic algorithms performed no better than conventional approaches to feature selection but required much more computation. On a difficult artificial machine-vision task, genetic algorithms were able to create new features (polynomial functions of the original features) which dramatically reduced classification error rates. Neural network and nearest-neighbor classifiers were unable to provide such low error rates using only the original featuresread more
Citations
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Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients
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A multi-feature and multi-channel univariate selection process for seizure prediction.
M. D'Alessandro,George Vachtsevanos,Rosana Esteller,Javier Echauz,Stephen D. Cranstoun,Greg Worrell,Landi M. Parish,Brian Litt,Brian Litt +8 more
TL;DR: A prospective, exploratory implementation of a seizure prediction method designed to adapt to individual patients with a wide variety of pre-ictal patterns, implanted electrodes and seizure types, which theoretically has the potential to address the challenge presented by the heterogeneity of EEG patterns seen in medication-resistant epilepsy.
References
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Pattern classification using neural networks
TL;DR: The author extends a previous review and focuses on feed-forward neural-net classifiers for static patterns with continuous-valued inputs, examining probabilistic, hyperplane, kernel, and exemplar classifiers.