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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Radhika Vikas Kulkarni
Sathyabama Institute of Science and Technology
Computer Science & IT Engineering

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Smart pools of data with ensembles for adaptive learning in dynamic data streams with class imbalance Radhika Vikas Kulkarni; S. Revathy; Suhas Haribhau Patil
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp310-318

Abstract

Streaming data incorporates dynamicity due to a nonstationary environment where data samples may endure class imbalance and change in data distribution over the period causing concept drifts. In real-life applications learning in dynamic data streams, is vitally important and challenging. A combined solution to adapt to class imbalance and concept drifts in dynamic data streams is rarely addressed by researchers. With this motivation, the current communication presents the online ensemble model smart pools of data with ensembles for class imbalance adaptive learning (SPECIAL) to learn in skewed and drifting data streams. It employs an ageing-based G-mean maximization strategy to adapt to dynamicity in data streams. It employs smart data-pools with the local expertise ensemble to classify samples lying in the same data-pool. The empirical and statistical study on different evaluation metrics exhibits that SPECIAL is more adaptive to class imbalanced dynamic data streams than the state-of-the-art algorithms.
Co-Authors S. Revathy Suhas Haribhau Patil