Smart adaptive ensemble model for multiclass imbalanced nonstationary data streams

In real-time streaming data, concept drift and class imbalance may occur simultaneously which causes the performance degradation of the online machine learning models. Most of the existing work is limited to addressing these issues for binary class data streams. Very little focus is given to the multi-class data streams. The most common approach to address these issues is ensemble learning. Ensemble learning consists of multiple classifiers combined which are trained on different subsets of the data to improve the overall accuracy. The performance of the ensemble learning approach suffers in case the new classifier is not trained on appropriate data (the data about the new concept). To address this gap, this study has proposed a Smart Adaptive Ensemble Model (SAEM) to address the issues of concept drift and class imbalance for multi-class data streams. The SAEM monitors the feature-level change in data distribution and creates a background ensemble to train the new classifier on features that observe change. To address the class imbalance issue, SAEM applies higher weights on the minority class instances using the dynamic class imbalance ratio. The proposed model outperformed the existing state-of-the-art approaches on the eight different data streams. The results showed an average improvement of 15.857% in accuracy, 20.35% in Kappa, 16.12% in F1-score, 15.58% in precision, and 16.42% in recall. The Friedman test confirmed statistically significant performance differences among all models across five key metrics. Based on the obtained results, the research findings strongly support the notion that SAEM exhibits enhanced effectiveness and efficiency as a solution for online learning applications.