Anjin Liu

According to our database, Anjin Liu authored at least 29 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

TS-DM: A Time Segmentation-Based Data Stream Learning Method for Concept Drift Adaptation.

Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang

IEEE Trans. Cybern., October, 2024

A self-adaptive ensemble for user interest drift learning.

Kun Wang, Li Xiong, Anjin Liu, Guangquan Zhang, Jie Lu

Neurocomputing, 2024

Early Concept Drift Detection via Prediction Uncertainty.

Pengqian Lu, Jie Lu, Anjin Liu, Guangquan Zhang

CoRR, 2024

2023

Concept Drift Detection Delay Index.

Anjin Liu, Jie Lu, Yiliao Song, Junyu Xuan, Guangquan Zhang

IEEE Trans. Knowl. Data Eng., May, 2023

Evolving Gradient Boost: A Pruning Scheme Based on Loss Improvement Ratio for Learning Under Concept Drift.

Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang, Li Xiong

IEEE Trans. Cybern., April, 2023

TCR-M: A Topic Change Recognition-based Method for Data Stream Learning.

Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang

Proceedings of the Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 27th International Conference KES-2023, 2023

An Augmented Learning Approach for Multiple Data Streams Under Concept Drift.

Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang

Proceedings of the AI 2023: Advances in Artificial Intelligence, 2023

2022

A Segment-Based Drift Adaptation Method for Data Streams.

Yiliao Song, Jie Lu, Anjin Liu, Haiyan Lu, Guangquan Zhang

IEEE Trans. Neural Networks Learn. Syst., 2022

Real-Time Prediction System of Train Carriage Load Based on Multi-Stream Fuzzy Learning.

Hang Yu, Jie Lu, Anjin Liu, Bin Wang, Ruimin Li, Guangquan Zhang

IEEE Trans. Intell. Transp. Syst., 2022

Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation.

Kun Wang, Jie Lu, Anjin Liu, Yiliao Song, Li Xiong, Guangquan Zhang

Neurocomputing, 2022

An Empirical Study of Fuzzy Decision Tree for Gradient Boosting Ensemble.

Zhaoqing Liu, Anjin Liu, Guangquan Zhang, Jie Lu

Proceedings of the AI 2021: Advances in Artificial Intelligence, 2022

2021

Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation.

Anjin Liu, Jie Lu, Guangquan Zhang

IEEE Trans. Neural Networks Learn. Syst., 2021

Concept Drift Detection: Dealing With Missing Values via Fuzzy Distance Estimations.

Anjin Liu, Jie Lu, Guangquan Zhang

IEEE Trans. Fuzzy Syst., 2021

Concept Drift Detection via Equal Intensity k-Means Space Partitioning.

Anjin Liu, Jie Lu, Guangquan Zhang

IEEE Trans. Cybern., 2021

Confident Anchor-Induced Multi-Source Free Domain Adaptation.

Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, Tongliang Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Bounds for Open-Set Learning.

Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Concept Drift Detection: Dealing with MissingValues via Fuzzy Distance Estimations.

Anjin Liu, Jie Lu, Guangquan Zhang

CoRR, 2020

Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation.

Anjin Liu, Jie Lu, Guangquan Zhang

CoRR, 2020

Fast Switch Naïve Bayes to Avoid Redundant Update for Concept Drift Learning.

Anjin Liu, Guangquan Zhang, Kun Wang, Jie Lu

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Real-Time Decision Making for Train Carriage Load Prediction via Multi-stream Learning.

Hang Yu, Anjin Liu, Bin Wang, Ruimin Li, Guangquan Zhang, Jie Lu

Proceedings of the AI 2020: Advances in Artificial Intelligence, 2020

An Elastic Gradient Boosting Decision Tree for Concept Drift Learning.

Kun Wang, Anjin Liu, Jie Lu, Guangquan Zhang, Li Xiong

Proceedings of the AI 2020: Advances in Artificial Intelligence, 2020

2019

Learning under Concept Drift: A Review.

Jie Lu, Anjin Liu, Fan Dong, Feng Gu, João Gama, Guangquan Zhang

IEEE Trans. Knowl. Data Eng., 2019

Knowledge graph-based entity importance learning for multi-stream regression on Australian fuel price forecasting.

Dennis Chow, Anjin Liu, Guangquan Zhang, Jie Lu

Proceedings of the International Joint Conference on Neural Networks, 2019

2018

Concept drift adaptation for learning with streaming data

Anjin Liu

PhD thesis, 2018

Accumulating regional density dissimilarity for concept drift detection in data streams.

Anjin Liu, Jie Lu, Feng Liu, Guangquan Zhang

Pattern Recognit., 2018

2017

Regional Concept Drift Detection and Density Synchronized Drift Adaptation.

Anjin Liu, Yiliao Song, Guangquan Zhang, Jie Lu

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Fuzzy time windowing for gradual concept drift adaptation.

Anjin Liu, Guangquan Zhang, Jie Lu

Proceedings of the 2017 IEEE International Conference on Fuzzy Systems, 2017

2016

An Online Competence-Based Concept Drift Detection Algorithm.

Anjin Liu, Guangquan Zhang, Jie Lu, Ning Lu, Chin-Teng Lin

Proceedings of the AI 2016: Advances in Artificial Intelligence, 2016

2014

Concept Drift Detection Based on Anomaly Analysis.

Anjin Liu, Guangquan Zhang, Jie Lu

Proceedings of the Neural Information Processing - 21st International Conference, 2014