*1.3. Feature Selection*

As mentioned in Section 1.2.2, feature selection is one of the most critical stages of ML modeling since it makes it possible to identify the best relation to the required complexity of the model and its quality at the preprocessing stage. In addition, feature selection makes it possible to find the more representative inputs in the real-world system and to eliminate no representative inputs or those that are redundant. ML models and training algorithms that consider only representative features improve their efficiency and reduce the time required for training [26,27,38,45]. A feature is an observable property in a system. Feature selection aims to select a specific subset of features that maximize the performance of the ML model.

The feature selection (FS) used here applied one of the most common techniques for removing irrelevant data, reducing dimensionality, increasing predictive accuracy and learning efficiency, and reducing the complexity of the final model [42]. Although there are different approaches for FS, all have theoretical support in their use of different methods, such as filtering, wrapping, and embedding, and involve techniques that use search algorithms, statistical criteria, and information, distance, dependence, and consistency measures [42]. The aim was to use linear and nonlinear methods to implement FS with a dataset acquired from an illumination radiation system.

This paper focuses on the feature selection stage in order to train a regression model to predict energy consumption in LED lights with specific light recipes in CPPSs. This stage is critical because it identifies the most representative features for training the model, and the other stages depend on it. These tools can enable further in-depth analysis of the energy savings that can be obtained with light recipes and pulsed and continuous light operation modes in artificial LED lighting systems.
