concept-drift
There are 90 repositories under concept-drift topic.
antoine-moulin/datastream-learning
Thanks to Latent Dirichlet Allocation and the ADWIN Algorithm, we realize topic modeling and concept drift detection among a corpus.
CCaribe9/SHAPEffects
Code and experiments related to SHAPEffects paper: 'A feature selection method based on Shapley values robust to concept shift in regression'
ChristophRaab/rrslvq
Code release of Reactive Robust Learning Vector Quantization
Ismailhachimi/Concept-Drift
Concept Drift Detection Through Resampling - Algorithms Implementation
RogersNtr/Handling-concept-drift
Code for testing Concept drift techniques on a real word dataset on a hexapod robot
saeedghoorchian/NCC-Bandits
Experiments for paper "Online Learning with Costly Features in Non-stationary Environments"
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.
chiachii/Learn.NSE-Algorithm
Implementation of Learn++.NSE Algorithm in Python
grahman20/ADF
Adaptive Decision Forest(ADF) is an incremental machine learning framework called to produce a decision forest to classify new records. ADF is capable to classify new records even if they are associated with previously unseen classes. ADF also is capable of identifying and handling concept drift; it, however, does not forget previously gained knowledge. Moreover, ADF is capable of handling big data if the data can be divided into batches.
GustavoHFMO/GMM-VRD
Algorithms proposed in the following paper: Oliveira, Gustavo HFM, Leandro L. Minku, and Adriano LI Oliveira. "GMM-VRD: A Gaussian Mixture Model for Dealing With Virtual and Real Concept Drifts." 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019.
hmgomes/StreamingRandomPatches
Repository for the StreamingRandomPatches algorithm implemented in MOA 2019.04
jchambyd/IGMN-NSE
Incremental Gaussian Mixture Network for Non-Stationary Environments
kmalialis/queue_based_resampling
Queue-Based Resampling (QBR, ICANN 2018)
Lucciola111/stream_autoencoder_windowing
Stream Autoencoder Windowing (SAW) - Change Detection Framework for high dimensional data streams
ATISLabs/SCARGC.jl
A Julia implementation of Stream Classification Algorithm Guided by Clustering – SCARGC
BogdanFloris/detecting-and-addressing-change
Code for my Master Thesis: How to detect and address changes in machine learning based data pipelines
DorinK/Association-Rules-for-Concept-Drifting
Final project in 'Tabular Data Science' course by Dr. Amit Somech at Bar-Ilan University.
grecosalvatore/drift-lens
Drift-Lens: an Unsupervised Drift Detection Framework for Deep Learning Classifiers on Unstructured Data
khaendler/HoeffdingPruningTree
An extension of the Hoeffding tree that prunes itself based on feature importance.
songqiaohu/CADM-plus
CADM+: Confusion-based Learning Framework With Drift Detection and Adaptation for Real-time Safety Assessment
ahhaque/FUSION
Efficient Multistream Classification using Direct DensIty Ratio Estimation
ATISLabs/EasyStream.jl
An extensible framework for data stream and concept drift in Julia
grahman20/TLF
We present a framework called TLF that builds a classifier for the target domain having only few labeled training records by transferring knowledge from the source domain having many labeled records. While existing methods often focus on one issue and leave the other one for the further work, TLF is capable of handling both issues simultaneously. In TLF, we alleviate feature discrepancy by identifying shared label distributions that act as the pivots to bridge the domains. We handle distribution divergence by simultaneously optimizing the structural risk functional, joint distributions between domains, and the manifold consistency underlying marginal distributions. Moreover, for the manifold consistency we exploit its intrinsic properties by identifying $k$ nearest neighbors of a record, where the value of k is determined automatically in TLF. Furthermore, since negative transfer is not desired, we consider only the source records that are belonging to the source pivots during the knowledge transfer. We evaluate TLF on seven publicly available natural datasets and compare the performance of TLF against the performance of eleven state-of-the-art techniques. We also evaluate the effectiveness of TLF in some challenging situations. Our experimental results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of the proposed framework over the state-of-the-art techniques.
jchambyd/LFDD
Landmark-based Feature Drift Detector
kmalialis/areba
Adaptive REBAlancing (AREBA, IEEE TNNLS 2021)
michaelchiucw/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.
michaelchiucw/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
ShikhaIIMA/Cognitive-Load-Detection-Ubittention
Machine Learning classifiers built to recognise 2 levels of cognitive load from sensor data of wearable wrist band (Microsoft Band 2) .
vvittis/DistributedLearningJava
Distributed Random Forest in Apache Flink
jkoessle/ODCD-Framework
Deep learning framework for concept drift detection. Part of a master thesis at the University of Mannheim.
kmalialis/actisiamese
ActiSiamese (Neurocomputing 2022)
michaelchiucw/SMOClust
The implementation of Synthetic Minority Oversampling based on stream Clustering (SMOClust)
minsu716-kim/Quilt
Quilt: Robust Data Segment Selection against Concept Drifts (AAAI 2024)
SalahuddinSwati/HighDimensionalDataStreamClassification
Learning High-Dimensional Evolving Data Streams With Limited Labels
SeongHyun-Seo/Concept-Drift-Detection-and-Adaptation
Concept Drift Detection and Adaptation Methods - Reference Codes and Papers
TxusLopez/streaming_lightHT
Light weight hyperparameter tuning for streaming scenarios