Artificial neural networks can be currently considered as one of the most important emerging tools in multivariate analysis due to their ability to deal with non-liner complex systems.In this work, a recently proposed neural network, called K-Contractive Map (K-CM), is presented and its performance in classification is evaluated towards other well-known classification methods. K-CM exploits the non-linear variable relationships provided by the Auto-CM neural network to obtain a fuzzy profiling of the samples and then applies the k-NN classifier to evaluate the class membership of samples. The algorithm Training with Input Selection and Testing (TWIST) is applied prior to K-CM to perform training/test data splitting for model parameter optimization and validation. This novel classification strategy was evaluated on ten different datasets and the obtained results were generally satisfactory. © 2014 Elsevier B.V.

Buscema, M., Consonni, V., Ballabio, D., Mauri, A., Massini, G., Breda, M., et al. (2014). K-CM: a new artificial neural network. Application to supervised pattern recognition. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 138, 110-119 [10.1016/j.chemolab.2014.06.013].

K-CM: a new artificial neural network. Application to supervised pattern recognition

CONSONNI, VIVIANA;BALLABIO, DAVIDE;TODESCHINI, ROBERTO
2014

Abstract

Artificial neural networks can be currently considered as one of the most important emerging tools in multivariate analysis due to their ability to deal with non-liner complex systems.In this work, a recently proposed neural network, called K-Contractive Map (K-CM), is presented and its performance in classification is evaluated towards other well-known classification methods. K-CM exploits the non-linear variable relationships provided by the Auto-CM neural network to obtain a fuzzy profiling of the samples and then applies the k-NN classifier to evaluate the class membership of samples. The algorithm Training with Input Selection and Testing (TWIST) is applied prior to K-CM to perform training/test data splitting for model parameter optimization and validation. This novel classification strategy was evaluated on ten different datasets and the obtained results were generally satisfactory. © 2014 Elsevier B.V.
Articolo in rivista - Articolo scientifico
classification; KCM; neural networks; chemometrics
English
2014
138
110
119
none
Buscema, M., Consonni, V., Ballabio, D., Mauri, A., Massini, G., Breda, M., et al. (2014). K-CM: a new artificial neural network. Application to supervised pattern recognition. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 138, 110-119 [10.1016/j.chemolab.2014.06.013].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/52664
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