Cluster based active learning for classification of evolving streams.

Autor: Himaja, D., Dondeti, Venkatesulu, Uppalapati, Srilakshmi, Virupaksha, Shashidhar
Zdroj: Evolutionary Intelligence; Aug2024, Vol. 17 Issue 4, p2167-2191, 25p
Abstrakt: Classification of imbalanced unlabelled data streams with concept drift in evolving streams has posed many challenges recently. Learner performance from the minority class is poor at high imbalance degrees. This causes drift detection to fail. Therefore, the existing model cannot be updated, resulting in poor classifier performance. Detecting drifts is typically done through supervised learning. They are impractical despite their effectiveness in detecting drifts. In real-world applications, only a portion of the data stream can be labelled as oracle assistance is pricey and laborious. To alleviate these problems, a novel technique which is a cluster based active learning for class imbalance and concept drift (CBAL) is presented in the paper. Adaptive sampling strategies are used for solving high imbalance degrees. A two-layer drift detection strategy is used for detecting drifts where the first layer is unsupervised and the second layer is supervised. To reduce the labelling cost this framework uses a clustering technique for querying the labels. Extensive experiments over synthetic and real-world data streams exhibit better classification performance. CBAL detects the drifts with fewer false alarms and with lesser oracle intervention. For high imbalanced case (i.e., 10%), the performance of CBAL is 53% and higher, whereas the performance of the other algorithms is zero or nil. The number of drifts detected by CBAL is much more accurate and it also reduces the labelling cost by 90%. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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