The Impact of Feature Selection on Balancing, Based on Diabetes Data

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Machado, D (Author) Other; Costa, VS (Author) FCUP; Pedro Brandão (Author) FCUP

Conference proceedings International

Title: BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, BIOSTEC 2023

Pages: 125-145

16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

Lisbon, PORTUGAL, FEB 16-18, 2023

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Other information

Authenticus ID: P-011-3FK

DOI: 10.1007/978-3-031-67088-6_7

Abstract (EN): Diabetes management data is composed of diverse factors and glycaemia indicators. Glycaemia predictive models tend to focus solely on glycaemia values. A comprehensive understanding of diabetes management requires the consideration of several aspects of diabetes management, beyond glycaemia. However, the inclusion of every aspect of diabetes management can create an overly high-dimensional data set. Excessive feature spaces increase computational complexity and may introduce over-fitting. Additionally, the inclusion of inconsequential features introduces noise that hinders a model's performance. Feature importance is a process that evaluates a feature's value, and can be used to identify optimal feature sub-sets. Depending on the context, multiple methods can be used. The drop feature method, in the literature, is considered to be the best approach to evaluate individual feature importance. To reach an optimal set, the best approach is branch and bound, albeit its heavy computational cost. This overhead can be addressed through a trade-off between the feature set's optimisation level and the process' computational feasibility. The improvement of the feature space has implications on the effectiveness of data balancing approaches. Whilst, in this study, the observed impact was not substantial, it warrants the need to reconsider the balancing approach given a superior feature space.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 21