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Error-Sensitive Grading for Model Combination
[chapter]
2005
Lecture Notes in Computer Science
Ensemble learning is a powerful learning approach that combines multiple classifiers to improve prediction accuracy. An important decision while using an ensemble of classifiers is to decide upon a way of combining the prediction of its base classifiers. In this paper, we introduce a novel grading-based algorithm for model combination, which uses cost-sensitive learning in building a meta-learner. This method distinguishes between the grading error of classifying an incorrect prediction as
doi:10.1007/11564096_74
fatcat:vzhntm2gebfx5mgzg72en2zblq