futtery,github

alibi-detect

Algorithms for outlier, adversarial and drift detection

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.

automl-streams

AutoML framework for implementing automated machine learning on data streams

cmgmm

Combine Merge GMM

concept-drift

This repo accompanies the FF22 research cycle focused on unsupervised methods for detecting concept drift

concept-drift-datasets-scikit-multiflow

concept drift datasets edited to work with scikit-multiflow directly

conceptdriftdatasets

popular concept drift evaluation datasets

conceptdriftinduction

Datasets for concept drift detection

d3-discriminative-drift-detector-concept-drift

unsupervised concept drift detection

driftdatasets

isotree

(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

koth-character-identifier

Finds out who you are from king of the hill by reading your tweets

machine-learning-for-time-series-with-python

Machine Learning for Time-Series with Python.Published by Packt

memstream

MemStream: Memory-Based Streaming Anomaly Detection

menelaus

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

meta-add

mygit

network-intrusion-detection

Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15

nsl-kdd

Network Security Analysis using Machine Learning on the NSL-KDD dataset from the KDD Cup 1999

nsl-kdd-1

PySpark solution to the NSL-KDD dataset: https://www.unb.ca/cic/datasets/nsl.html

nsl-kdd-network-intrusion-detection

Machine Learning Algorithms on NSL-KDD dataset

oasw-concept-drift-detection-and-adaptation

An online learning method used to address concept 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.