A review of data complexity measures and their applicability to pattern classification problems
José Salvador Sánchez Garreta, José Martínez Sotoca, Ramón A. Mollineda · 2005
It is widely accepted that the empirical behavior of classifiers strongly depends on available data. For a given problem, it is rather dicult to guess which classifier will provide the best performance. Traditional experimental studies consist of presenting accuracy of a set of classifiers on a small number of problems, without analyzing why a classifier outperforms other classification algorithms. Recently, some researchers have tried to characterize data complexity and relate it to classifier performance. In this paper, we present a review of data complexity measures in the framework of pattern classification and discuss possible applications to a number of practical problems.