Cascading classifiers
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Cascading is a particular case of ensemble learning based on the concatenation of several classifiers, using all information collected from the output from a given classifier as additional information for the next classifier in the cascade. Unlike voting or stacking ensembles, which are multiexpert systems, cascading is a multistage one.
See also
References
- J. Gama and P. Brazdil. Cascade Generalization. Machine Learning, 41(3):315--343, 2000. [1]
- J. Minguillón. On Cascading Small Decision Trees. PhD dissertation. Universitat Autònoma de Barcelona, 2002. [2]
- H. Zhao and S. Ram. Constrained Cascade Generalization of Decision Trees. IEEE Transactions on Knowledge and Data Engineering, 16(6):727--739, 2004.