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Unveiling the Role of Diversity Measure in Concept Drift Detection: A Comparative Analysis and Future Prospects
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Author(s): Osamah A. Mahdi (Melbourne Institute of Technology, Australia), Nawfal Ali (Monash University, Australia), Eric Pardede (La Trobe University, Australia)and Bhagwan Das (Melbourne Institute of Technology, Australia)
Copyright: 2025
Pages: 32
Source title:
Enhancing Data-Driven Electronics Through IoT
Source Author(s)/Editor(s): Bhagwan Das (Centre for Artificial Intelligence Research and Optimization (AIRO), Torrens University Australia, Australia), Muhammad Zakir Shaikh (Universidad de Málaga, Spain), Samreen Hussain (Dawood University of Engineering and Technology, Pakistan)and Enrique Nava Baro (Universidad de Málaga, Spain)
DOI: 10.4018/979-8-3693-5448-3.ch001
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Abstract
Concept drift, marked by changes in the statistical properties of a target variable, is a notable challenge in machine learning, data mining and applications involving big data and large-scale data processing. Addressing this, the employment of diversity measure has emerged as an effective strategy. This chapter examine and investigate the role of the diversity measure in detecting concept drift and compare and analysis four different ways of using them: DMDDM for drift` detection in a fully supervised binary classification context, DMDDM-S in a semi-supervised context, DMODD for online drift detection in a fully supervised multi-classification context, and HBBE, a hybrid block-based ensemble designed for addressing different types of concept drifts. The comparative analysis evaluates the efficacy of these methods and the results confirm their effectiveness within their respective settings. Overall, this chapter explores advancements in using diversity measures for concept drift detection in big data, emphasizing their importance and future research in machine learning contexts.
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