Bird’s Eye View feature selection for high-dimensional data

Abstract

In machine learning, an informative dataset is crucial for accurate predictions. However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. To tackle this challenge, the Bird's Eye View (BEV) feature selection technique is introduced. This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. The proposed strategy in this paper leads to improved classification performance and a reduced number of features compared to conventional methods, as demonstrated by outperforming state-of-the-art feature selection techniques across multiple benchmark datasets.

More by Dr. Zarina Oflaz

• Optimal adaptive control of methane production in anaerobic digestion via reinforcement learningJournal of Process Control · 2026
• Development of machine learning models for intelligent prediction of workability, mechanical, and durability characteristics of cement mortar made of recycled sandConstruction and Building Materials · 2026 · Citations: 1
• Leveraging eco-friendly pozzolans and machine learning for reduced cement use in soil improvement of road base layersEuro-Mediterranean Journal for Environmental Integration · 2026
• Comparative Analysis of Prediction Accuracy in Clean Energy Forecasting: Implications for Pricing and Profitability in Wind and Biogas Energy SystemsSpringer proceedings in earth and environmental sciences · 2026