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Advisor(s)
Abstract(s)
The latest technological advances drive the emergence of countless real-time data streams fed by users, sensors, and devices. These data sources can be mined with the help of predictive and classification techniques to support decision-making in fields like e-commerce, industry or health. In particular, stream-based classification is widely used to categorise incoming samples on the fly. However, the distribution of samples per class is often imbalanced, affecting the performance and fairness of machine learning models. To overcome this drawback, this paper proposes Bplug, a balancing plug-in for stream-based classification, to minimise the bias introduced by data imbalance. First, the plug-in determines the class imbalance degree and then synthesises data statistically through non-parametric kernel density estimation. The experiments, performed with real data from Wikivoyage and Metro of Porto, show that Bplug maintains inter-feature correlation and improves classification accuracy. Moreover, it works both online and offline.
Description
Keywords
Data bias Fairness Imbalanced data sets Machine learning algorithm Stream classification
Citation
de Arriba-Pérez, F., García-Méndez, S., Leal, F., Malheiro, B., Burguillo-Rial, J.C. (2024). Balancing Plug-In for Stream-Based Classification. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F., Colla, V. (eds) Information Systems and Technologies. WorldCIST 2023. Lecture Notes in Networks and Systems, vol 799. Springer, Cham. https://doi.org/10.1007/978-3-031-45642-8_6
Publisher
Springer