Abstract
This chapter is concerned with ensemble classification, i.e. using a set of classifiers to classify unseen data rather than just a single one. The classifiers in the ensemble all predict the correct classification of each unseen instance and their predictions are then combined using some form of voting system.
The idea of a random forest of classifiers is introduced and issues relating to the selection of a different training set and/or a different set of attributes from a given dataset when constructing each of the classifiers are discussed.
A number of alternative ways of combining the classifications produced by an ensemble of classifiers are considered. The chapter concludes with a brief discussion of a distributed processing approach to dealing with the large amount of computation often required to generate an ensemble.
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Bramer, M. (2020). Ensemble Classification. In: Principles of Data Mining. Undergraduate Topics in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-7493-6_14
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DOI: https://doi.org/10.1007/978-1-4471-7493-6_14
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