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Feature Selection for High-Dimensional Genomic Microarray Data Export

In Proc. 18th International Conf. on Machine Learning (2001), pp. 601-608.

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We report on the successful application of feature selection methods to a classification problem in molecular biology involving only 72 data points in a 7130 dimensional space. Our approach is a hybrid of filter and wrapper approaches to feature selection. We make use of a sequence of simple filters, culminating in Koller and Sahami's (1996) Markov Blanket filter, to decide on particular feature subsets for each subset cardinality. We compare between the resulting subset cardinalities using...


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