An Interpretable Feature Selection Method Integrating Ensemble Models for Chiller Fault Diagnosis

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Performance comparison

The following FSMs were selected for comparison with our IFS-IEM：

• Mutual Information (MI)
• Feature Importance (FI)
• Recursive Feature Elimination (RFE)
• Max-Relevance and Min-Redundancy (mRMR)
• Back-tracing sequential forward feature selection (BT-SFS)
• ReliefF-RFECV

The models include SVM, KNN, DT, DBN, DNN, CNN, RF, XGBoost, CatBoost, LightGBM were used for fault diagnosis performance testing. If you are interested in the design of these models, you can refer to FD test.ipynb

Citation

If you use this code for your research, please cite our paper:

Bi, J., Wang, H., Hua, M., & Yan, K. (2024). An interpretable feature selection method integrating ensemble models for chiller fault diagnosis. Journal of Building Engineering, 109029.