A Study of Meta-Learning in Ensemble Based Classifier
نویسندگان
چکیده
-The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is wellknown that ensemble methods can be used for improving prediction performance. Researchers from various disciplines such as statistics and AI considered the use of ensemble methodology. Meta-learning is a technique that seeks to compute higher-level classifiers (or classification models), called meta-classifiers, that integrate in some principled fashion multiple classifiers computed separately over different databases. This study, describes metalearning and presents Stacking, SCANN (Stacking, Correspondence Analysis and Nearest Neighbor), Arbiter Trees, Combiner Trees, Grading and the JAM system (Java Agents for Meta-learning).
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