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Txus López's Projects

cai2023 icon cai2023

Concept Drift and Uncertainty in Industrial AI

curie icon curie

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.

iris4java icon iris4java

Java code working on the iris dataset using the WEKA library (for learning purposes)

iris4py icon iris4py

Python code working on the iris dataset using the scikit-learn library (learning purposes)

streaming_ccpp icon streaming_ccpp

Streaming Regressors for Electrical Power Prediction in a Combined Cycle Power Plant

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