针对流数据优化超参时，提出四种边界约束处理方法，效果优于传统策略。
Constrained Hyperparameter Optimization for Streaming Data
- 设计四种新方法处理超参数的边界约束问题。
- 在真实数据集上验证，新方法显著提升在线学习性能。
- 适合研究流数据实时优化或自适应模型的开发者。
超参数优化对获得最优模型性能至关重要。现有研究多集中于批处理学习场景，而流数据带来的复杂性仍具挑战。因此，在线学习阶段实现超参数的自我调整成为关键目标。许多超参数具有取值范围限制，导致优化操作生成无效解。为解决此问题，必须采用边界约束处理技术以修正无效解。本文提出四种有效管理约束数值优化中边界约束的策略。近期基于启发式与进化算法的方法采用“边界”策略，即超出边界的参数值被重置到边界上限。本研究通过在标准数据集上的实证分析表明，所提策略优于传统的“边界”策略。
原文摘要 · Abstract (English)
Optimization of hyperparameters is a critical factor to obtain optimal model performance. While existing research has predominantly concentrated on batch-learning scenarios, addressing the complexities inherent in data streams presents a challenge. The deployment of sophisticated methodologies to manage data streams becomes highly important. Consequently, the capacity for self-adjusting hyperparameters during on-line learning phases emerges as a goal. Many hyperparameters exhibit constraints and are confined within bounded search spaces, rendering specific solutions unacceptable upon applying optimization operators. To solve this issue, employing boundary constraint- handling techniques becomes imperative to rectify invalid solutions. This paper presents strategies for effectively managing boundary constraints within constrained numerical optimization problems. Recent methodologies, including heuristic and evolutionary-based optimization, employ a "boundary" strategy, wherein values that surpass boundary thresholds for a given hyperparameter are realigned to the respective limits. Our study introduces four strategies to navigate boundary constraints in online optimization algorithms. Through empirical investigations conducted on established datasets, we demonstrate that adopting boundary strategies outperforms the "boundary" strategy.
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