Topic: "stream-learning"
TxusLopez/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.
Language: Python - Size: 194 KB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 3 - Forks: 1
Angel3245/Stream-Learning-for-Stock-Forecasting
This project involves the analysis of stream learning techniques for stock price forecasting using the River library.
Language: Jupyter Notebook - Size: 6.81 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 1 - Forks: 0
kmalialis/actisiamese
ActiSiamese (Neurocomputing 2022)
Language: Python - Size: 1.11 MB - Last synced at: about 1 year ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 3
Venoli/Asips-for-Pulsar-Astronomy
'Asips' is a Research conducted for automating the pulsar star candidate selection process. This is the API of Asips which can be used by anyone. This implementation uses the HTRU2 dataset.
Language: Python - Size: 41.9 MB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 2
kmalialis/siameseduo
SiameseDuo++ (Neurcomputing 2025)
Size: 1.95 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0
kmalialis/augmented_queues
Augmented Queues (IEEE SSCI 2022)
Language: Python - Size: 18.5 MB - Last synced at: about 1 year ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 1
BaltiBoix/3W_TFM Fork of petrobras/3W
Detection and classification of anomalous events in oil extraction. Incremental learning methods applied to the Petrobras 3W dataset.
Language: Jupyter Notebook - Size: 751 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0
