LASSO multi-objective learning algorithm for feature selection
(2020) Soft Computing — Vol. 24, n° 4, p. 9 (2020)
Files
LASSOmulti-objectivelearningalgorithmforfeatureselection.pdf
Open Access - Adobe PDF
- 1.41 MB
Details
- Authors
- Abstract
- 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.
- Affiliations
Citations
Coelho, F., Costa, M., Verleysen, M., & Braga, A. P. (2020). LASSO multi-objective learning algorithm for feature selection. Soft Computing, 24(4), 9. https://doi.org/10.1007/s00500-020-04734-w (Original work published 2020)