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Cited by 86 publications
(64 citation statements)
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“…The LTree algorithm of Gama [8] embodies a general framework for learning functional trees, multivariate classification or regression trees that can use combinations of attributes at decision nodes, leaf nodes, or both.…”
Section: Related Workmentioning
confidence: 99%
“…The LTree algorithm of Gama [8] embodies a general framework for learning functional trees, multivariate classification or regression trees that can use combinations of attributes at decision nodes, leaf nodes, or both.…”
Section: Related Workmentioning
confidence: 99%
“…This approach is further explored by [3,10] which use meta-rules drawn from experimental studies, to help predict the applicability of different algorithms; the rules consider measurable characteristics of the data (e.g., number of examples, number of attributes, number of classes, kurtosis, etc. ).…”
Section: Related Workmentioning
confidence: 99%
“…The idea is to define a set of domain characteristics or meta-features that are relevant for predicting the performance of the given learning algorithms (Aha, 1992;Michie, Spiegelhalter, & Taylor, 1994;Gama & Brazdil, 1995;Brazdil, 1998;Keller, Paterson, & Berrer, 2000;Brazdil, Soares, & Pinto da Costa, 2003). Meta-features may include information concerning error rates of base-learners, so called landmarkers Pfahringer, Bensusan, & Giraud-Carrier 2000) or the structure of induced decision trees (Bensusan, 1998;Peng et al, 2002).…”
Section: Exploiting Meta-knowledge With a Set Of Learning Algorithmsmentioning
confidence: 99%