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Name: Txus López
Type: User
Name: Txus López
Type: User
Concept Drift and Uncertainty in Industrial AI
Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learning models must detect and adapt to such changes, so as to exhibit a good predictive performance after a drift has occurred. In this regard, the development of effective drift detection algorithms becomes a key factor in data stream mining. In this work we propose CU RIE, a drift detector relying on cellular automata. Specifically, in CU RIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream. Computer simulations are presented and discussed to show that CU RIE, when hybridized with other base learners, renders a competitive behavior in terms of detection metrics and classification accuracy. CU RIE is compared with well-established drift detectors over synthetic datasets with varying drift characteristics.
Java code working on the iris dataset using the WEKA library (for learning purposes)
Python code working on the iris dataset using the scikit-learn library (learning purposes)
Streaming Regressors for Electrical Power Prediction in a Combined Cycle Power Plant
Cellular automata for real-time machine learning in the presence of concept drift
Gaussian Receptive Fields for Stream Learning
Light weight hyperparameter tuning for streaming scenarios
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