Heterogeneous Drift Learning: Classification of Mix-Attribute Data with Concept Drifts-Reference-Cited by-同舟云学术

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Heterogeneous Drift Learning: Classification of Mix-Attribute Data with Concept Drifts

Published:2022-10-13 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)
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Author:

Zhao Lang¹,Zhang Yiqun¹,Ji Yuzhu¹,Zeng An¹,Gu Fangqing²,Luo Xiaopeng³

Affiliation:

1. Guangdong University of Technology,School of Computer Science and Technology,Guangzhou,China

2. Guangdong University of Technology,School of Mathematics and Statistics,Guangzhou,China

3. BNU-HKBU United International College,Department of Computer Science,Zhuhai,China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10032305/10032324/10032342.pdf?arnumber=10032342

Reference42 articles.

1. A Survey on Ensemble Learning for Data Stream Classification

2. Dynamic and Heterogeneous Ensembles for Time Series Forecasting

3. A Unified Entropy-Based Distance Metric for Ordinal-and-Nominal-Attribute Data Clustering

4. Enabling fast prediction for ensemble models on data streams

5. Concept drift-tolerant transfer learning in dynamic environments;yang;IEEE Transactions on Neural Networks and Learning Systems,2021

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1. Clustering by Learning the Ordinal Relationships of Qualitative Attribute Values;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Learning Hierarchical Representations in Temporal and Frequency Domains for Time Series Forecasting;Pattern Recognition and Computer Vision;2023-12-26

3. Unsupervised Concept Drift Detection via Imbalanced Cluster Discriminator Learning;Pattern Recognition and Computer Vision;2023-12-24

4. Selecting Heterogeneous Features Based on Unified Density-Guided Neighborhood Relation for Complex Biomedical Data Analysis;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05

5. Review on novelty detection in the non-stationary environment;Knowledge and Information Systems;2023-11-30

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