Abstract
We here compare the performance (predictive accuracy and processing time) of different neural network ensembles with that of nearest neighbor classifier ensembles. Concerning the connectionist models, the multilayer perceptron and the modular neural network are employed. Experiments on several real-problem data sets demonstrate a certain superiority of the nearest-neighbor-based schemes, in terms of both accuracy and computing time. When comparing the neural network ensembles, one can observe a better behavior of the multilayer perceptron than that of the modular networks.
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Valdovinos, R.M., Sánchez, J.S. (2007). Performance Analysis of Classifier Ensembles: Neural Networks Versus Nearest Neighbor Rule. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_15
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DOI: https://doi.org/10.1007/978-3-540-72847-4_15
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