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"This embedded method is based on a perceptron, a type of artificial neural net-work that can be seen as the simplest kind of feedforward neu-ral network, namely, a linear classifier. This method is based on training a perceptron in a supervised learning context. Interconnection weights are used to indicate the most relevant features and provide a ranking."
Seijo-Pardo, B., Porto-Díaz, I., Bolón-Canedo, V., & Alonso-Betanzos, A. (2017). Ensemble feature selection: Homogeneous and heterogeneous approaches. Knowledge-Based Systems, 118, 124–139. https://doi.org/10/f9qgrv
Mejia-Lavalle, M., Sucar, L., & Arroyo-Figueroa, G. (2006). Feature selection with a perceptron neural net. Proceedings of the International Workshop on Feature Selection for Data Mining, 131–135.
-> No idea if there is an R implementation
The text was updated successfully, but these errors were encountered:
"This embedded method is based on a perceptron, a type of artificial neural net-work that can be seen as the simplest kind of feedforward neu-ral network, namely, a linear classifier. This method is based on training a perceptron in a supervised learning context. Interconnection weights are used to indicate the most relevant features and provide a ranking."
Seijo-Pardo, B., Porto-Díaz, I., Bolón-Canedo, V., & Alonso-Betanzos, A. (2017). Ensemble feature selection: Homogeneous and heterogeneous approaches. Knowledge-Based Systems, 118, 124–139. https://doi.org/10/f9qgrv
Mejia-Lavalle, M., Sucar, L., & Arroyo-Figueroa, G. (2006). Feature selection with a perceptron neural net. Proceedings of the International Workshop on Feature Selection for Data Mining, 131–135.
-> No idea if there is an R implementation
The text was updated successfully, but these errors were encountered: