Awesome-OL Models

A comprehensive collection of online learning strategies and classifiers for your machine learning projects

Note: This page provides an overview of all available models in Awesome-OL. For detailed usage instructions, please refer to the User Guide section.

Key Insights and Overview

Model Diversity

Awesome-OL provides a comprehensive collection of models covering various aspects of online learning:

5 advanced OAL strategies with cognitive and submodular approaches
8 supervised classifiers with specialized drift adaptation techniques
2 semi-supervised classifiers for scenarios with limited labeled data
7 baseline methods for performance comparison

Feature Highlights

The toolkit excels in several key areas:

Drift Adaptation: 12/17 methods include drift adaptation capabilities
Multi-class Support: 10/17 methods support multi-class classification
Ensemble Methods: 5 powerful ensemble approaches for improved stability
Imbalanced Learning: Specialized methods like ROALE-DI and OLI2DS

Performance Considerations

When selecting a method, consider:

For concept drift: IWDA, ARF, and DES show strong performance
For active learning: DSA-AI and CogDQS provide advanced query strategies
For efficiency: QRBLS and BLS-W offer fast incremental updates
For imbalanced data: ROALE-DI and OLI2DS include specialized handling

🧠 OAL Strategies

Explore a variety of online active learning strategies in the OAL_strategies/ folder.

Strategy	Description	Reference	Code	ear	Journal/Conference
CogDQS	Dual-query strategy using human memory cognition	IEEE	—	2023	TNNLS
DSA-AI	Dynamic submodular learning for imbalanced drifting streams	IEEE	GitHub	2024	TNNLS
MTSGQS	Memory-triggered submodularity-guided strategy	IEEE	—	2023	TITS
DMI-DD	Explanation-based query strategy at chunk level	IEEE	GitHub	2024	TCYB

Baseline Strategies

Strategy	Description	Reference	Code	Year	Journal/Conference
RS	Random Sampling	—	—	—	—
US_fix	Uncertainty sampling with fixed threshold	IEEE	—	2014	TNNLS
US_var	Uncertainty sampling with variable threshold	IEEE	—	2014	TNNLS

⚙️ OAL Classifiers

Classifier	Description	Reference	Source	Year	Journal/Conference
ROALE-DI	Reinforcement-based ensemble for drifting imbalanced data	IEEE	GitHub	2022	TKDE
OALE	Online ensemble with hybrid labeling	IEEE	—	2019	TNNLS

🔍 OSSL Classifiers

Classifier	Description	Reference	Source	Year	Journal/Conference
OSSBLS	Semi-supervised BLS with static anchors	IEEE	—	2021	TII
ISSBLS	Semi-supervised BLS without historical dependency	IEEE	—	2021	TII

Baseline Strategy

Classifier	Description	Reference	Year	Journal/Conference
SOSELM	Semi-supervised ELM	ScienceDirect Paper	2016	Neurocomputing

📊 Supervised Classifiers

Classifier	Description	Reference	Source	Year	Journal/Conference
OLI2DS	Imbalanced data stream learner with dynamic costs	IEEE	GitHub	2023	TKDE
IWDA	Learner-agnostic drift adaptation using density estimation	IEEE	GitHub	2023	TNNLS
DES	Drift-adaptive ensemble with SMOTE	IEEE	GitHub	2024	TNNLS
ACDWM	Adaptive chunk selection for stability and drift	IEEE	GitHub	2020	TNNLS
ARF	Adaptive resampling ensemble with ADWIN	Springer	GitHub	2017	Machine Learning
SRP	Random subspace + online bagging	IEEE	GitHub	2019	ICDM
BLS-W	Online BLS with Sherman–Morrison–Woodbury update	IEEE	GitHub	2023	TCYB
QRBLS	BLS with QR factorization	IEEE	GitHub	2025	TNNLS

Baseline Classifier

Classifier	Description	Reference	Source	Year	Journal/Conference
OSELM	Sequential ELM without drift detection	IEEE	GitHub	2006	TNNLS

🧩 Summary of Features

Method	OAL Strategy	Classifier	Binary	Multi-class	Drift Adaptation	Ensemble
ROALE-DI	✅	✅	✅	✅	✅	✅
CogDQS	✅	—	✅	✅	✅	—
DSA-AI	✅	—	✅	✅	✅	—
DMI-DD	✅	—	✅	✅	✅	—
MTSGQS	✅	—	✅	✅	✅	—
RS	✅	—	✅	✅	—	—
US-fix	✅	—	✅	✅	—	—
US-var	✅	—	✅	✅	—	—
OLI2DS	—	✅	✅	—	✅	—
IWDA	—	✅	✅	✅	✅	✅
DES	—	✅	✅	—	✅	✅
ACDWM	—	✅	✅	—	✅	✅
SRP	—	✅	✅	✅	✅	✅
ARF	—	✅	✅	✅	✅	✅
QRBLS	—	✅	✅	✅	—	—

Table of Contents

Awesome-OL Models

Key Insights and Overview

Model Diversity

Feature Highlights

Performance Considerations

🧠 OAL Strategies

Baseline Strategies

⚙️ OAL Classifiers

🔍 OSSL Classifiers

Baseline Strategy

📊 Supervised Classifiers

Baseline Classifier

🧩 Summary of Features