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

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.