.png){kind=icon}
.png){kind=icon}
Return
Stream-learn-open-source Python library for difficult data stream batch analysis
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2021.10.120.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a stream generator, which allows producing a synthetic data stream that may incorporate each of the three main concept drift types (i.e., sudden, gradual and incremental drift) in their recurring or non-recurring version, as well as static and dynamic class imbalance. The package allows conducting experiments following established evaluation methodologies (i.e., Test-Then-Train and Prequential). Besides, estimators adapted for data stream classification have been implemented, including both simple classifiers and state-of-the-art chunk-based and online classifier ensembles. The package utilises its own implementations of prediction metrics for imbalanced binary classification tasks to improve computational efficiency. (c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Data stream
Concept drift
Imbalanced data
Dynamic class imbalance
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W