Abstract
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.
Highlights
F ROM the perspective of safety, schedule and cost, key performance indicators (KPIs), e.g., the core parameters of major equipments and product quality variables, are vital during construction or production [1], [2]
The main contributions of this paper are threefold: (1) An improved ensemble feature selection based on decision tree (EFS-decision tree (DT)) strategy is developed for KPI prediction, which implements preliminary evaluation and revaluation of variable scores
We summarize the main steps of regression DT as follows: First, traverse the variable j and the splitting point s to find the optimal splitting variable and point (j, s) that meet the following conditions:
Summary
F ROM the perspective of safety, schedule and cost, key performance indicators (KPIs), e.g., the core parameters of major equipments and product quality variables, are vital during construction or production [1], [2]. This study developes an improved ensemble feature selection based on DT (EFS-DT) strategy for KPI prediction. The ensemble feature selection fuses multi-criteria filtering results to retain more informative variables at less time cost. Differences in criteria between selectors often cause significant divergences in feature scores, known as evidence conflicts in decision fusion. Song [33] developed a conflict management method based on evidence belief divergence and information volume. (2) Ensemble feature selection is utilized to fuse multi-criteria filtering results to retain more informative variables with less time cost. The selected variable sets are used for modeling, and the prediction performance of the models is evaluated
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