Abstract
The main prerequisite for the efficient use of ensemble systems is that the base classifiers should be diverse among themselves. One way of increasing diversity is through the use of feature distribution methods in ensemble systems. In this paper, an investigation of the use of feature distribution methods among the classifiers of ensemble systems will be performed. In this investigation, five different methods of data distribution will be used. These ensemble systems will use six existing combination methods, in which four of them are fusion-based methods and the remaining two are selection-based methods. As a result, it is aimed to detect which ensemble systems are more suitable to use feature distribution among the classifier.
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Santana, L.E.A., Canuto, A.M.P., Xavier, J.C. (2008). Using Feature Distribution Methods in Ensemble Systems Combined by Fusion and Selection-Based Methods. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_26
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DOI: https://doi.org/10.1007/978-3-540-87536-9_26
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