Imbalanced Data Stream Classification using Dynamic Ensemble Selection
This addresses classification challenges in streaming data for applications like real-time monitoring, but it is incremental as it combines existing techniques.
The paper tackled imbalanced data stream classification with concept drift by proposing a framework that integrates data pre-processing and dynamic ensemble selection, achieving significantly more accuracy in experiments on six artificially generated streams with varying imbalance ratios and concept drifts.
Modern streaming data categorization faces significant challenges from concept drift and class imbalanced data. This negatively impacts the output of the classifier, leading to improper classification. Furthermore, other factors such as the overlapping of multiple classes limit the extent of the correctness of the output. This work proposes a novel framework for integrating data pre-processing and dynamic ensemble selection, by formulating the classification framework for the nonstationary drifting imbalanced data stream, which employs the data pre-processing and dynamic ensemble selection techniques. The proposed framework was evaluated using six artificially generated data streams with differing imbalance ratios in combination with two different types of concept drifts. Each stream is composed of 200 chunks of 500 objects described by eight features and contains five concept drifts. Seven pre-processing techniques and two dynamic ensemble selection methods were considered. According to experimental results, data pre-processing combined with Dynamic Ensemble Selection techniques significantly delivers more accuracy when dealing with imbalanced data streams.