论文标题
使用特征重要性融合来对安全 - 关键系统的机器学习输出进行更可靠的解释
Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems using Feature Importance Fusion
论文作者
论文摘要
当机器学习支持安全至关重要系统中的决策时，重要的是要验证和理解产生特定输出的原因。尽管特征重要性计算方法有助于解释，但缺乏关于特征的重要性的量化方式，这使得为结果提供了大多数不可靠的解释。解决缺乏一致性的可能解决方案是结合多个特征重要性量化器的结果，以减少估计的方差。我们的假设是，这将导致对每个功能对机器学习预测的贡献的更强大和可信赖的解释。为了协助检验这一假设，我们提出了一个可扩展的框架，分为四个主要部分：（i）传统数据预处理和预测机器学习模型的准备； （ii）预测机学习； （iii）使用集合策略的重要性量化和（iv）特征性决策融合。我们还引入了一种新颖的融合度量，并将其与最先进的融合度进行了比较。我们的方法经过综合数据的测试，那里是众所周知的地面真相。我们比较了不同的融合方法及其在训练和测试集中的结果。我们还研究了数据集中的不同特征如何影响所研究的特征重要性集合。结果表明，与现有方法相比，我们的特征重要合奏框架总体上会产生特征重要性错误15％。此外，结果表明，数据集中的不同级别的噪声不会影响特征的重要性集合准确量化特征重要性的能力，而特征重要性量化误差随特征的数量和正交信息的数量而增加。
When machine learning supports decision-making in safety-critical systems, it is important to verify and understand the reasons why a particular output is produced. Although feature importance calculation approaches assist in interpretation, there is a lack of consensus regarding how features' importance is quantified, which makes the explanations offered for the outcomes mostly unreliable. A possible solution to address the lack of agreement is to combine the results from multiple feature importance quantifiers to reduce the variance of estimates. Our hypothesis is that this will lead to more robust and trustworthy interpretations of the contribution of each feature to machine learning predictions. To assist test this hypothesis, we propose an extensible Framework divided in four main parts: (i) traditional data pre-processing and preparation for predictive machine learning models; (ii) predictive machine learning; (iii) feature importance quantification and (iv) feature importance decision fusion using an ensemble strategy. We also introduce a novel fusion metric and compare it to the state-of-the-art. Our approach is tested on synthetic data, where the ground truth is known. We compare different fusion approaches and their results for both training and test sets. We also investigate how different characteristics within the datasets affect the feature importance ensembles studied. Results show that our feature importance ensemble Framework overall produces 15% less feature importance error compared to existing methods. Additionally, results reveal that different levels of noise in the datasets do not affect the feature importance ensembles' ability to accurately quantify feature importance, whereas the feature importance quantification error increases with the number of features and number of orthogonal informative features.
