A meta-learning framework for pattern classification by means of data complexity measures
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Autores: Ramón Alberto Mollineda Cardenas
, J. S. Sanchez , José Martínez Sotoca
- Localización: Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN-e 1988-3064, ISSN 1137-3601, Vol. 10, Nº. 29, 2006 (Ejemplar dedicado a: Minería de Datos), págs. 31-38
- Idioma: español
- DOI: 10.4114/ia.v10i29.875
- Enlaces
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Resumen
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.
