RENT-Repeated Elastic Net Technique for Feature Selection

• Published in: IEEE access Vol. 9; pp. 152333 - 152346
• Main Authors: Jenul, Anna, Schrunner, Stefan, Liland, Kristian Hovde, Indahl, Ulf Geir, Futsaether, Cecilia Marie, Tomic, Oliver
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data models; Data science; Datasets; Elastic net regularization; ensemble feature selection; exploratory analysis; Feature extraction; Feature selection; Generalized linear models; Performance prediction; Predictive models; Regularization; selection stability; Selectors; Stability; Stability criteria; Statistical models; Task analysis; Training; Training data
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3126429

Feature selection is an essential step in data science pipelines to reduce the complexity associated with large datasets. While much research on this topic focuses on optimizing predictive performance, few studies investigate stability in the context of the feature selection process. In this study, we present the Repeated Elastic Net Technique (RENT) for Feature Selection. RENT uses an ensemble of generalized linear models with elastic net regularization, each trained on distinct subsets of the training data. The feature selection is based on three criteria evaluating the weight distributions of features across all elementary models. This fact leads to the selection of features with high stability that improve the robustness of the final model. Furthermore, unlike established feature selectors, RENT provides valuable information for model interpretation concerning the identification of objects in the data that are difficult to predict during training. In our experiments, we benchmark RENT against six established feature selectors on eight multivariate datasets for binary classification and regression. In the experimental comparison, RENT shows a well-balanced trade-off between predictive performance and stability. Finally, we underline the additional interpretational value of RENT with an exploratory post-hoc analysis of a healthcare dataset.