Unveiling the Role of Diversity Measure in Concept Drift Detection: A Comparative Analysis and Future Prospects

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.