A novel and Intelligent Ensemble Framework for Real-Time Detection and Adaptation to Concept Drift in Data Streams Using Incremental Decision Trees

Subject Areas : Computer Engineering and IT

Hadi Tarazodar ¹ , Karamollah Bagherifard ^{2 *} , Samad Nejatian ³ , Hamid Parvin ⁴ , Razieh Malekhosseini ⁵

1 - Department of computer Engineering ,Yas.C., Islamic Azad University ,Yasuj, Iran
2 - Department of computer Engineering ,Yas.C., Islamic Azad University ,Yasuj, Iran
3 - Department of Electrical Engineering ,Yas.C., Islamic Azad University ,Yasuj, Iran
4 - Department of computer Engineering , NoM.C., Islamic Azad University , Noorabad Mamasani, Iran
5 - Department of computer Engineering ,Yas.C., Islamic Azad University ,Yasuj, Iran

Received: 2025-07-09 Accepted : 2025-09-11 Published : 2025-10-13

Keywords: Concept Drift, Data Stream, Incremental Decision Tree, Hoeffding, Ensemble Learning,

Abstract :

Learning from real-time data has been increasingly considered over the past decade. The change in data distribution in online learning, known as concept drift, reduces the accuracy of learning models and makes them ineffective in future predictions. This research aims to design and develop a novel ensemble incremental decision tree algorithm that is capable of detecting concept drift and automatically adapting to changes in data distribution. To achieve this goal, a new architecture of ensemble incremental decision tree is presented that uses an adaptive probabilistic sampling strategy to continuously monitor the pattern of data changes and automatically and in real time performs structural updates in the decision tree. Unlike traditional methods that respond reactively to changes, this approach has an active monitoring mechanism that enables early detection of concept drift by tracking changes in the model error function. In this way, the proposed model is able to maintain high accuracy even in streaming data scenarios with irregular changes. Extensive experiments were conducted on the dataset and the results show that the proposed method performs better than existing methods in several evaluation criteria including accuracy, recall, and precision.

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