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cdcms

The implementation of the Concept Drift handling based on Clustering in the Model Space (CDCMS) algorithm, proposed in the paper “A Diversity Framework for Dealing with Multiple Types of Concept Drift Based on Clustering in the Model Space”, accepted by IEEE TNNLS 2020.

data-stream-learning

deep-river

diversitypool

The implementation of the Diversity Pool algorithm, proposed in the paper "Diversity-Based Pool of Models for Dealing with Recurring Concepts" and presented at IJCNN '18

imbalanced-stream-generator

MOA compatible imbalanced data stream generator, as described in "The impact of data difficulty factors on classification of imbalanced and concept drifting data streams".

moa

MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.

river

🌊 Online machine learning in Python

smoclust

The implementation of Synthetic Minority Oversampling based on stream Clustering (SMOClust)

stream-learn

The stream-learn is an open-source Python library for difficult data stream analysis.

uob-oob-multiclass

The multi-class version of Oversampling and Undersampling Online Bagging for class imbalanced data stream learning. It can be applied both to multi-class and binary classification problems.