Improved Ensemble Feature Selection Based on DT for KPI Prediction

In the production process of large-scale machinery and complex industries, the key performance indicator (KPI) prediction is an essential part of project scheduling and cost estimation. The continuous enrichment of sensor types and functions brings us massive soft-sensing parameters for regression, but also brings severe challenges to algorithm learning. In this paper, an improved ensemble feature selection based on decision tree (EFS-DT) strategy for KPI prediction is developed. On the one hand, the ensemble of multi-criteria filtering results broadens the selector’s perspective without the time cost of superposition. On the other hand, credibility and similarity analysis are designed to eliminate the concerns of Dempster’s combination rule about conflict. After re-evaluating the variable scores, more high-quality variables can be selected to build a more accurate and robust KPI prediction model. Finally, a realistic shield tunnel case in China is used to evaluate the feasibility and effectiveness of proposed approach