DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction (2206.08974v1)

Abstract: This paper discusses the critical decision process of extracting or selecting the features in a supervised learning context. It is often confusing to find a suitable method to reduce dimensionality. There are pros and cons to deciding between a feature selection and feature extraction according to the data's nature and the user's preferences. Indeed, the user may want to emphasize the results toward integrity or interpretability and a specific data resolution. This paper proposes a new method to choose the best dimensionality reduction method in a supervised learning context. It also helps to drop or reconstruct the features until a target resolution is reached. This target resolution can be user-defined, or it can be automatically defined by the method. The method applies a regression or a classification, evaluates the results, and gives a diagnosis about the best dimensionality reduction process in this specific supervised learning context. The main algorithms used are the Random Forest algorithms (RF), the Principal Component Analysis (PCA) algorithm, and the multilayer perceptron (MLP) neural network algorithm. Six use cases are presented, and every one is based on some well-known technique to generate synthetic data. This research discusses each choice that can be made in the process, aiming to clarify the issues about the entire decision process of selecting or extracting the features.