Mapping classifiers and datasets

Given the posterior probability estimates of 14 classifiers on 38 datasets, we plot two-dimensional maps of classifiers and datasets using principal component analysis (PCA) and Isomap. The similarity between classifiers indicate correlation (or diversity) between them and can be used in deciding whether to include both in an ensemble. Similarly, datasets which are too similar need not both be used in a general comparison experiment. The results show that (i) most of the datasets (approximately two third) we used are similar to each other, (ii) multilayer perceptrons and k-nearest neighbor variants are more similar to each other than support vector machine and decision tree variants, (iii) the number of classes and the sample size has an effect on similarity.

Research highlights
► We plotted 2-d maps of classifiers and datasets using PCA and Isomap.
► Approximately two third of the UCI datasets are similar to each other.
► MLP and KNN variants are more similar to each other than SVM and DT variants.