number of features versus number of instances? (small data sets)
Budget: $10 – $30 USD
writing about the relation between number of features and number of instances?
https://academic.oup.com/bioinformatics/article/21/8/1509/249540
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
https://academic.oup.com/bioinformatics/article/21/8/1509/249540
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