Abstract
Data streams represent an ongoing stream of data, in many forms, coming from different sources. In real time data often comes in streams and is changing over time. Concept drift in supervised learning means that the data is going through a change. While solving predictive maintenance tasks on the streaming data, traditional models, trained on historical data, may become invalid, when such change occurs. Hence, the learning models need to adapt to changes very quick and accurately. Adaptive ensemble models are used for classification on data streams. In this paper, we implemented the modifications of the adaptive bagging methods, which uses internal class-weighting schemes for the model adaptation. Implemented models were evaluated on manually created data streams with ensemble methods and analyzed performance evaluation of different classifiers. This performance is greatly differed than the traditional model and hence handles the drift in much more effective way.
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Patil, R., Patil, P., Ghongade, A., Dsa, A., Lokhande, P., Munot, H. (2022). Online System for Identifying Need of Machine Maintenance by Mining Data Streams and Handling Concept Drifts. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, KL. (eds) Sentimental Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1408. Springer, Singapore. https://doi.org/10.1007/978-981-16-5157-1_76
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DOI: https://doi.org/10.1007/978-981-16-5157-1_76
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