Feature Selection Based on Three-Dimensional Correlation Graphs

The process of feature selection is a critical component of any decision-making system incorporating machine or deep learning models applied to multidimensional data. Feature selection on input data can be performed using a variety of techniques, such as correlation-based methods, wrapper-based methods, or embedded methods. However, many conventionally used approaches do not support backwards interpretability of the selected features, making their application in real-world scenarios impractical and difficult to implement. This work addresses that limitation by proposing a novel correlation-based strategy for feature selection in regression tasks, based on a three-dimensional visualization of correlation analysis results—referred to as three-dimensional correlation graphs. The main objective of this study is the design, implementation, and experimental evaluation of this graphical model through a case study using a multidimensional dataset with 28 attributes. The experiments assess the clarity of the visualizations and their impact on regression model performance, demonstrating that the approach reduces dimensionality while maintaining or improving predictive accuracy, enhances interpretability by uncovering hidden relationships, and achieves better or comparable results to conventional feature selection methods.