LASSO multi-objective learning algorithm for feature selection

doi:10.1007/s00500-020-04734-w

LASSO multi-objective learning algorithm for feature selection

2020-02-03

17

PRE

AI

F

Frederico Coelho *

M

Marcelo Azevedo Costa

DOI：10.1007/s00500-020-04734-w

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Abstract

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This work proposes a new algorithm for training neural networks to solve the problems of feature selection and function approximation. The algorithm applies different weight constraint functions for the hidden and the output layers of a multilayer perceptron neural network. The LASSO operator is applied to the hidden layer; therefore, the training provides automatic selection of relevant features and the standard norm regularization function is applied to the output layer. Therefore, we propose a multi-objective training algorithm that is able to select the important features while solving the approximation problem.

Keywords:

Supervised learning

Feature selection

Multi-objective

LASSO

LASSO multi-objective learning algorithm for feature selection

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