面向流数据的受限超参数优化
Constrained Hyperparameter Optimization for Streaming Data
查看机构详情
- University of Porto（波尔图大学）
- INESC TEC
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
针对流数据超参数优化需处理边界约束的问题，提出四种在线优化算法的边界约束策略，经实证验证其性能优于现有“边界”策略。
中文摘要 AI 辅助
超参数优化是获得最优模型性能的关键因素。现有研究主要集中在批量学习场景，而处理数据流固有的复杂性是一项挑战，部署复杂的方法来管理数据流变得非常重要，因此在线学习阶段自调整超参数的能力成为一个目标。许多超参数存在约束，被限制在有界搜索空间内，这使得应用优化算子时特定解决方案不可接受。为解决此问题，采用边界约束处理技术来修正无效解决方案势在必行。本文提出了在受限数值优化问题中有效处理边界约束的策略。近期的方法，包括基于启发式和进化的优化，采用“边界”策略，即把超参数超过边界阈值的值重新调整到相应的极限。本研究介绍了在线优化算法中处理边界约束的四种策略。通过在已建立的数据集上进行的实证研究，我们证明采用边界策略的方法优于“边界”策略。
英文摘要
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.
