Neural lasso: a unifying approach of lasso and neural networks

Abstract

AbstractIn recent years, there has been a growing interest in establishing bridges between statistics and neural networks. This article focuses on the adaptation of the widely used lasso algorithm within the context of neural networks. To accomplish this, the network configuration is first designed. After that, in order to estimate the network weights, three optimization algorithms are considered. The first one, called standard neural lasso, employs the conventional procedure for training neural networks. The second optimization algorithm, termed restricted neural lasso, mimics traditional lasso to establish a connection between statistics and machine learning. Finally, a third optimization algorithm, called voting neural lasso was developed. Voting neural lasso offers a novel way of estimating weights by considers the significance of variables across the cross-validation scenarios. Results showed that the conventional approach of training neural networks resulted in a lower performance when the validation set is not sufficiently representative. It was also observed that restricted neural lasso and the traditional lasso obtained equivalent results, which shows the convergence of the neural technique with the statistical one. Finally, the developed voting neural lasso algorithm outperformed the traditional lasso. These results were obtained across diverse training sets, encompassing observations ranging from as few as 47 to as many as 4000, with the number of predictors varying from 9 to 200.