ArticleBMC bioinformatics2024
Fractal feature selection model for enhancing high-dimensional biological problems.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A hybrid deep learning and quantum computing framework enhances cybersecurity threat detection and analysis.Scientific reports · 2026Article
- Integrating Taguchi design and machine learning models for trait stability and predicting forage quality in naturally occurring grass pea (Lathyrus spp.).BMC plant biology · 2026Article
- IHBOFS: A Biomimetics-Inspired Hybrid Breeding Optimization Algorithm for High-Dimensional Feature Selection.Biomimetics (Basel, Switzerland) · 2025Article
- Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury.Frontiers in genetics · 2025Article
- Article
- Strategies for overcoming data scarcity, imbalance, and feature selection challenges in machine learning models for predictive maintenance.Scientific reports · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%.
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Registered trials
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