concept-drift

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convol…

streaming timeseries time-series lstm generative-adversarial-network gan rnn autoencoder ensemble-learning trees active-learning concept-drift graph-convolutional-networks interpretability anomaly-detection adversarial-attacks explaination anogan unsuperivsed nettack

UpdatedMay 22, 2024
Python

Western-OC2-Lab /AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics

Star629

Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning

iot machine-learning deep-learning model-selection data-preprocessing feature-engineering hyperparameter-tuning concept-drift automl intrusion-detection-system automated-machine-learning data-streams python-examples data-stream-processing python-samples iot-data-analytics

UpdatedMay 14, 2024
Jupyter Notebook

IFCA-Advanced-Computing /frouros

Star252

Frouros: an open-source Python library for drift detection in machine learning systems.

python machine-learning statistics concept-drift machine-learning-engineering mle change-detection covariate-shift mlops drift-detection machine-learning-operations data-drift distribution-shift dataset-drift dataset-shift

UpdatedFeb 11, 2026
Python

Western-OC2-Lab /PWPAE-Concept-Drift-Detection-and-Adaptation

Star219

Data stream analytics: Implement online learning methods to address concept drift and model drift in data streams using the River library. Code for the paper entitled "PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams" published in IEEE GlobeCom 2021.

iot machine-learning data-stream lightgbm adaptive-learning ensemble-learning drift online-learning streaming-data real-time-analytics concept-drift change-detector intrusion-detection-system anomaly-detection iot-data-analytics drift-detection cicids2017

UpdatedJun 5, 2023
Jupyter Notebook

whyisyoung /CADE

Star144

Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications

machine-learning tensorflow keras artificial-intelligence ids autoencoder mlp explanation concept-drift interpretability explainable-ai explainable-ml xai machine-learning-security drebin self-supervised-learning contrastive-learning ids2018

UpdatedMar 25, 2023
Python

alipsgh /tornado

Star129

The Tornado 🌪️ framework, designed and implemented for adaptive online learning and data stream mining in Python.

data-stream adaptive-learning ddm online-learning adwin concept-drift incremental-learning drift-detection fhddm mddm eddm hddm

UpdatedOct 31, 2023
Python

yfzhang114 /OneNet

Star125

This is an official PyTorch implementation of the NeurIPS 2023 paper 《OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling》

machine-learning online-learning concept-drift time-series-analysis

UpdatedNov 27, 2024
Python

blablahaha /concept-drift

Star119

Algorithms for detecting changes from a data stream.

adwin concept-drift change-detector page-hinckley

UpdatedOct 21, 2018
Python

flytxtds /AutoGBT

Star114

AutoGBT is used for AutoML in a lifelong machine learning setting to classify large volume high cardinality data streams under concept-drift. AutoGBT was developed by a joint team ('autodidact.ai') from Flytxt, Indian Institute of Technology Delhi and CSIR-CEERI as a part of NIPS 2018 AutoML for Lifelong Machine Learning Challenge.

hyperopt concept-drift automl nips-2018 lifelong-ml

UpdatedDec 13, 2019
Python

SJTU-DMTai /DoubleAdapt

Star112

The official API of DoubleAdapt (KDD'23), an incremental learning framework for online stock trend forecasting, WITHOUT dependencies on the qlib package.

quantitative-finance concept-drift incremental-learning stock-prediction meta-learning distribution-shift kdd2023

UpdatedDec 25, 2024
Python

awesome-mlops /awesome-ml-monitoring

Star93

A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀

data-science machine-learning deep-learning datascience dataops data-management data-cleaning model-management concept-drift data-quality aiops machine-learning-platform data-monitoring mlops model-monitoring model-performance data-drift model-explainability model-drift

UpdatedMay 7, 2024

Stream-AD /MemStream

Star89

MemStream: Memory-Based Streaming Anomaly Detection

streaming real-time denial-of-service concept-drift fraud-detection anomaly-detection memory-based multi-aspect-record intrusion-dectection

UpdatedJan 10, 2024
Python

zelros /cinnamon

Star77

CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system

machine-learning monitoring drift streaming-data concept-drift domain-adaptation covariate-shift explainable-ai mlops drift-detection drift-correction

UpdatedDec 9, 2022
Python

mitre /menelaus

Star68

Online and batch-based concept and data drift detection algorithms to monitor and maintain ML performance.

data-science machine-learning statistics concept-drift drift-detection data-drift

UpdatedDec 27, 2023
Python

Western-OC2-Lab /OASW-Concept-Drift-Detection-and-Adaptation

Star55

An online learning method used to address concept drift and model drift. Code for the paper entitled "A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams" published in IEEE Internet of Things Magazine.

iot machine-learning random-forest svm data-stream xgboost hyperparameter-optimization lightgbm drift bayesian-optimization particle-swarm-optimization online-learning real-time-analytics concept-drift change-detector intrusion-detection-system anomaly-detection nsl-kdd iot-data-analytics drift-detection

UpdatedJan 20, 2024
Jupyter Notebook

Western-OC2-Lab /AutoML-and-Adversarial-Attack-Defense-for-Zero-Touch-Network-Security

Star43

This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.