Feature Importance Ranking Using Interval-Valued Methods and Aggregation Functions for Machine Learning Applications
Source abstract
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: crossref · Quality: baseline
Research context
OpenAlex topics: Statistical and Computational Modeling · Machine Learning and Data Classification · Data Mining Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Exact and Technical Sciences, University of Rzeszów, Al. Rejtana 16C, 35-959 Rzeszów, Poland
References (23)
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