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- W2490559115 abstract "This chapter presents the state of the art in classifier ensembles and their comparative performance analysis. The main aim and focus of this chapter is to present and compare the author’s recently developed neural network based classifier ensembles. The three types of neural classifier ensembles are considered and discussed. The first type is a classifier ensemble that uses a neural network for all its base classifiers. The second type is a classifier ensemble that uses a neural network as one of the classifiers among many of its base classifiers. The third and final type is a classifier ensemble that uses a neural network as a fusion classifier. The chapter reviews recent neural network based ensemble classifiers and compares their performances with other machine learning based classifier ensembles such as bagging, boosting, and rotation forest. The comparison is conducted on selected benchmark datasets from UCI machine learning repository." @default.
- W2490559115 created "2016-08-23" @default.
- W2490559115 creator A5026528338 @default.
- W2490559115 date "2012-01-01" @default.
- W2490559115 modified "2023-09-24" @default.
- W2490559115 title "Neural Network Based Classifier Ensembles" @default.
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- W2490559115 doi "https://doi.org/10.4018/978-1-4666-1833-6.ch014" @default.
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