arXiv：cs.LG（机器学习，全量分类）· Tugrul Cabir Hakyemez, Ener Uras Gokhan·· 14 小时前AI 评分33
通过多目标超参数优化校准 RUL 预测时效性
Calibrating Prediction Timeliness Through Multi-Objective Hyperparameter Optimization for Remaining Useful Life Prediction
AI 导读
一项研究将超参数优化目标本身作为设计变量，在 NASA C-MAPSS 涡扇和 BackBlaze 硬盘基准上评估 MLP、LSTM、XGBoost、TCN、Transformer 五种架构的剩余使用寿命（RUL）预测。
正文
Abstract:In predictive maintenance, early and late RUL prediction errors carry asymmetric consequences, yet hyperparameter optimization typically targets a single accuracy metric that treats both directions equally. This study treats the optimization objective itself as a design variable. Five architectures (MLP, LSTM, XGBoost, TCN, and Transformer) are evaluated under three regimes: single-objective maximization of $R^2$, single-objective minimization of the NASA scoring function, and a multi-objective formulation that jointly optimizes both criteria. The multi-objective search employs NSGA-II with Entropy-CRITIC weighting for Pareto selection. Seventy-five model-dataset-strategy combinations are assessed on the NASA C-MAPSS turbofan and BackBlaze hard-disk drive benchmarks. On C-MAPSS, all strategies achieve comparable accuracy ($R^2 \approx 0.89$), yet multi-objective optimization reduces directional imbalance by approximately 33%, improving calibration of early versus late predictions. Model rankings prove configuration-dependent, with simpler architectures frequently outperforming deeper temporal models. On BackBlaze, the objectives shift from complementary to conflicting, producing divergent Entropy-CRITIC weights and a substantial generalization gap (best $R^2 \approx 0.34$). These results demonstrate that the optimization objective materially shapes prognostic behavior and that multi-objective search provides a practical mechanism for calibrating prediction timeliness in RUL modeling.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.01530 [cs.LG] |
| (or arXiv:2610.01530v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01530 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tugrul Hakyemez [view email]
[v1]
Thu, 1 Oct 2026 12:06:11 UTC (60 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org