Work

Feature Importance Ranking Using Interval-Valued Methods and Aggregation Functions for Machine Learning Applications

Year: 2025

Type: article

Abstract: Feature selection is one of the key stages in the process of creating machine learning models and conducting data analysis. This paper presents the results of research related to the implementation of a new algorithm for feature selection and ranking based on weighted interval aggregations. It took into account interval importance values obtained from dividing the dataset into subsets. The algorithm was highly effective in identifying relevant features. The results of comparative studies with nine known methods of feature importance assessment are presented. Ten synthetic datasets and five real datasets were used for the experiments. The calculations also included tests of the relevance of the results obtained. In most experiments, the IVWFR algorithm proved to be the best, achieving the best classification results after identifying subsets of relevant features.

Source: Applied Sciences

DOI: 10.3390/app152212130

Authors: Aleksander Wojtowicz, Wiesław Paja, Urszula Bentkowska

Institution: University of Rzeszów

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Uniwersytet Rzeszowski