Resumen de A meta-learning framework for pattern classification by means of data complexity measures
Ramón Alberto Mollineda Cardenas, J. S. Sanchez, José Martínez Sotoca
It is widely accepted that the empirical behavior of classifiers strongly depends on available data. For a given problem, it is rather difficult to guess which classifier will provide the best performance or to set a proper expectation on classification 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 general meta-learning framework based on a number of data complexity measures. We also discuss the applicability of this method to several problems in pattern analysis.
