DYNAMIC ENSEMBLE LEARNING: ADAPTIVE FUSION OF HETEROGENEOUS MODELS FOR EVOLVING DATA STREAMS

Abstract

In the era of big data, the continuous influx of streaming data poses significant challenges for machine learning models, particularly in maintaining their accuracy and relevance over time. Traditional ensemble learning techniques have shown promise in improving predictive performance by combining multiple base models. However, existing ensemble methods often lack adaptability to changing data distributions and may struggle with handling evolving concepts in streaming data. This research proposes a novel approach, termed Dynamic Ensemble Learning (DEL), which focuses on the adaptive fusion of heterogeneous models to effectively capture and adapt to evolving patterns in data streams. DEL leverages techniques from online learning, ensemble methods, and concept drift detection to dynamically adjust the ensemble composition in response to changes in the underlying data distribution. Through extensive experimentation and comparative analysis, this paper demonstrates the effectiveness of DEL in achieving superior predictive performance and adaptability compared to existing ensemble methods, particularly in scenarios with evolving data streams and concept drifts. Additionally, practical applications and implications of DEL in real-world scenarios are discussed, highlighting its potential to enhance decision-making processes in various domains, including finance, healthcare, and environmental monitoring.

The advent of big data has revolutionized the landscape of data analytics, ushering in an era where the sheer volume and velocity of streaming data pose significant challenges for traditional machine learning models. In particular, maintaining the accuracy and relevance of these models over time in the face of evolving data distributions and concept drifts has become a pressing concern. While ensemble learning has emerged as a powerful technique for improving predictive performance by aggregating multiple base models, its static nature often fails to adapt effectively to the dynamic nature of streaming data. To address this gap, this paper introduces a novel approach called Dynamic Ensemble Learning (DEL), which focuses on the adaptive fusion of heterogeneous models to effectively capture and respond to evolving patterns in data streams. DEL leverages concepts from online learning, ensemble methods, and concept drift detection to dynamically adjust the ensemble composition in real- time, thereby enabling it to adapt to changes in the underlying data distribution. Through extensive experimentation and evaluation, we demonstrate the effectiveness of DEL in achieving superior predictive performance and adaptability compared to traditional ensemble methods, especially in scenarios characterized by evolving data streams and concept drifts. Additionally, we discuss practical applications and potential implications of DEL across various