  Characteristics extracted from the training datasets of classification
problems have proven to be effective predictors in a number of meta-analyses.
Among them, measures of classification complexity can be used to estimate the
difficulty in separating the data points into their expected classes.
Descriptors of the spatial distribution of the data and estimates of the shape
and size of the decision boundary are among the known measures for this
characterization. This information can support the formulation of new
data-driven pre-processing and pattern recognition techniques, which can in
turn be focused on challenges highlighted by such characteristics of the
problems. This paper surveys and analyzes measures which can be extracted from
the training datasets in order to characterize the complexity of the respective
classification problems. Their use in recent literature is also reviewed and
discussed, allowing to prospect opportunities for future work in the area.
Finally, descriptions are given on an R package named Extended Complexity
Library (ECoL) that implements a set of complexity measures and is made
publicly available.

Machine Learning;Machine Learning