Abstract
The analysis of voluminous patterns is often problematic due to the confounding effect of features that are not relevant to the problem at hand. For instance, the classification of biomedical spectra is often best achieved through the identification of a subset of highly discriminatory features while ignoring the non-relevant ones. With respect to pattern classification, we present a feature reduction strategy, which begins with the instantiation of many classifiers operating on different subsets of features, employing a feature sampling method to identify discriminatory feature subsets. These subsets are further aggregated to improve the overall performance of the underlying classifiers. We empirically demonstrate, using a voluminous biomedical dataset, that this strategy produces superior classification accuracies compared against a set of benchmarks.
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Pizzi, N.J. (2012). A Feature Reduction Strategy for the Analysis of Voluminous Biomedical Patterns. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31718-7_14
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DOI: https://doi.org/10.1007/978-3-642-31718-7_14
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