Feature selection and over-adaptation prevention in neural networks using the auxiliary weight method

Abstract

In this study, we propose a method called auxiliary weight (AW) for neural networks in which each input value is weighted according to its contribution to the input dimension. AW is similar to Lasso regularization in the sense that it can extract features; however, AW is faster than Lasso in processing data that contains a several contributing dimensions and massive non-contributing dimensions, such as the data of medical mass spectrometry. (Code:https://bitbucket.org/akira_you/awexperiment)