A novel ensemble L1 regularization based variable selection framework with an application in near infrared spectroscopy

- The variable selection problem is mapped into a constrained optimization problem and L1 regularization method is adopted to find the best combination of input variables.
- An ensemble framework is built to improve the stability and robustness of variable selection methods, in which more than one variable selector is designed.
- The performance of proposed method is investigated through two public datasets in the area of near infrared spectroscopy.

Variable selection is an essential part during the whole process of qualitative and quantitative analysis of spectroscopy. Traditional methods like interval partial least square (iPLS), uninformative variable elimination (UVE), successive projections algorithm (SPA) etc. often have some disadvantages such as many parameters need to be tuned, weak robustness and so on. To solve these problems, this paper proposed a novel variable selection framework which combines UVE algorithm and ensemble L1 regularization framework together. The whole process of proposed method includes two phases: rough and fine selection. Firstly, UVE algorithm is used to eliminate the uninformative variables (rough selection). Secondly, the variable selection problem is mapped into a L1 regularization optimization problem with constraint (fine selection). To improve the stability and robustness of proposed method, an ensemble variable selection framework is designed which ensemble the results of many L1 regularization selectors. To validate the performance of proposed method, the following two public near infrared spectral datasets were tested: (1) Spectral (range from 900 nm to 1700 nm) and octane data of gasoline; (2) Spectral (range from 1100 nm to 2498 nm) and moisture data of corn. The experimental results showed that the proposed method can not only select the most featured wavelengths, but also can improve the stability and robustness of variable selection results.