A study on concept drift detection algorithms for real-world data streams

Abdul Razak M S 1, Naseer R 1, Sreenivasa B R 2, * and Nirmala C R 1

1 Department of CSE, Bapuji Institute of Engineering & Technology, Davangere, India.
2 Department of ISE, Bapuji Institute of Engineering & Technology, Davangere, India.
 
Research Article
International Journal of Science and Research Archive, 2024, 11(02), 1301–1305.
Article DOI: 10.30574/ijsra.2024.11.2.0593
Publication history: 
Received on 27 February 2024; revised on 06 April 2024; accepted on 09 April 2024
 
Abstract: 
The arrival of data has changed in today's digital environment, becoming more dynamic. Dynamic data is characterized by its speed, variety, and infinite size. Data streams are one category of dynamic data. To address the issues with data streams, several strategies and AI models were developed. One such problem is concept drift, which results from changes in the data's distribution and eventually lowers the performance of the AI model. This means that regular updates to the model are required. In our work, we will analyse the performance using evaluation metrics and compare the effectiveness of the current error-based methods with window-based methods for real-world datasets.
 
Keywords: 
Concept Drift; Data Stream; Drift Algorithms; Classification; Prediction Error.
 
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