number of features versus number of instances? (small data sets)

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writing about the relation between number of features and number of instances? [login to view URL] Two conclusions can safely be drawn from this study. First, the behavior of the optimal-feature-size relative to the sample size depends strongly on the classifier and the feature-label distribution. An immediate corollary is that one should be wary of rules-of-thumb generalized from specific cases. Second, the performance of a designed classifier can be greatly influenced by the number of features and therefore one should attempt to use a number close to the optimal number. This means that it can be useful to refer to a database of optimal-feature-size curves to choose a feature size, even if this means making a necessarily very coarse approximation of the distribution model from the data—even perhaps just a visual assessment of the data. Owing to the roughness of these kinds of approximations, a classifier like the polynomial SVM, which shows strong robustness with respect to large feature sets, has inherent advantages over a classifier like LDA, which does not show robustness

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Data Science

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