Abstract
Combining single classifiers into larger ensembles is an established method for improving the accuracy. Of course, the obvious improvement is bound up with increased storage space (memory) and computational burden. However this trade-off is easy to accept with modern computers. Classifiers can be combined at the level of features or data subsets and by the use of different classifiers or different combiners, see Figure 3.1. Popular methods are bagging and boosting which are meta algorithms for learning different classifiers.
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Scherer, R. (2012). Ensemble Techniques. In: Multiple Fuzzy Classification Systems. Studies in Fuzziness and Soft Computing, vol 288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30604-4_3
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DOI: https://doi.org/10.1007/978-3-642-30604-4_3
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