论文标题
部分可观测时空混沌系统的无模型预测
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data
论文作者
论文摘要
根据手头的任务，错误可能不会产生相同的后果。然而，研究有限的研究研究了错误向量中不同特征的贡献的不平衡的影响。因此，我们提出功能影响平衡（FIB）得分。它衡量了两个向量之间的差异中特征是否存在平衡的影响。我们设计了FIB分数，位于[0，1]中。得分接近0表示，少数功能会导致大多数误差，而得分接近1的分数表明大多数功能都会均等地造成误差。我们使用自动编码器和各种自动编码器在不同数据集上实验研究FIB。我们展示了功能影响平衡在训练过程中如何变化，并展示其可用性，以支持单输出和多输出任务的模型选择。
Errors might not have the same consequences depending on the task at hand. Nevertheless, there is limited research investigating the impact of imbalance in the contribution of different features in an error vector. Therefore, we propose the Feature Impact Balance (FIB) score. It measures whether there is a balanced impact of features in the discrepancies between two vectors. We designed the FIB score to lie in [0, 1]. Scores close to 0 indicate that a small number of features contribute to most of the error, and scores close to 1 indicate that most features contribute to the error equally. We experimentally study the FIB on different datasets, using AutoEncoders and Variational AutoEncoders. We show how the feature impact balance varies during training and showcase its usability to support model selection for single output and multi-output tasks.
