A bi-level structured classifier integrating unsupervised and supervised machine learning models

Yichen Liu; Zitong Zhang; Chunlei Zhang; Kai Zhang

doi:10.1117/12.2667225

22 February 2023 A bi-level structured classifier integrating unsupervised and supervised machine learning models

Yichen Liu, Zitong Zhang, Chunlei Zhang, Kai Zhang

Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 125871X (2023) https://doi.org/10.1117/12.2667225
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China

Abstract

In this paper, we propose a bi-level structured classifier integrating unsupervised and supervised machine learning models, which aims to improve the model's decision-making ability on classification boundaries by dividing the sample subspace to make full use of the multivariate attribute features and spatial structure of the data. The bi-level structured classifier utilizes the unsupervised clustering algorithms for subspace partitioning of sample data in the first layer, and selects the applicable supervised models to learn on the subspace samples in the second layer. We conduct a case study on a lithology dataset from the complex carbonate reservoirs for lithology identification. The classification results indicate that the bi-level integrated classifier (98.77%) is superior to the machine learning models (XGBoost: 97.67 %). And the ability of the bi-level integrated architecture is verified in effectiveness and generalization, and effectively improves the classification performance.

Citation Download Citation

Yichen Liu, Zitong Zhang, Chunlei Zhang, and Kai Zhang "A bi-level structured classifier integrating unsupervised and supervised machine learning models", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 125871X (22 February 2023); https://doi.org/10.1117/12.2667225

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KEYWORDS

Machine learning

Data modeling

Statistical modeling

Education and training

Performance modeling

Integrated modeling

Statistical analysis

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