A Combined Approach To Detect Key Variables In Thick Data Analytics
This addresses feature selection for industrial problems, but it appears incremental as it builds on existing methods like permutation tests and compares to Lasso without claiming major breakthroughs.
The paper tackles the problem of selecting significant predictor variables in machine learning by proposing an approach using permutation tests to identify the most informative ones, and demonstrates its application in chemical analysis with a comparison to Lasso.
In machine learning one of the strategic tasks is the selection of only significant variables as predictors for the response(s). In this paper an approach is proposed which consists in the application of permutation tests on the candidate predictor variables in the aim of identifying only the most informative ones. Several industrial problems may benefit from such an approach, and an application in the field of chemical analysis is presented. A comparison is carried out between the approach proposed and Lasso, that is one of the most common alternatives for feature selection available in the literature.