神经网络生成响应曲线的反事实解释
Counterfactual Explanations of Neural Network-Generated Response Curves
AI总结：
本文提出用基于遗传算法的反事实解释方法识别影响神经网络响应曲线形状的关键被动特征，并在合成与作物产量数据上验证了其有效性。
摘要：
响应曲线展示了一个敏感系统对变化刺激的响应幅度。然而，此类系统的响应可能对多个刺激（即输入特征）敏感，而这些刺激不一定是相互独立的。因此，为选定的输入特征（称为“主动特征”）生成的响应曲线的形状可能依赖于其他输入特征（称为“被动特征”）的取值。在本工作中，我们考虑系统响应通过回归神经网络进行近似的情形。我们提出使用反事实解释（CFEs）来识别对神经网络黑箱生成的响应曲线形状具有最高相关性的特征。CFEs由一种基于遗传算法的方法生成，该方法求解一个多目标优化问题。具体而言，给定一条为主动特征生成的响应曲线，CFE寻找需要修改的被动特征的最小组合，以改变响应曲线的形状。我们在一个具有1维输入的合成数据集和两个具有2维输入的作物产量预测数据集上测试了我们的方法。在合成数据集上获得的特征及特征组合的相关性排序与用于生成该问题的方程的分析结果一致。在产量预测数据集上获得的结果表明，被动特征对肥料响应性的影响取决于每块田地的地形特征。
英文摘要：
Response curves exhibit the magnitude of the response of a sensitive system to a varying stimulus. However, response of such systems may be sensitive to multiple stimuli (i.e., input features) that are not necessarily independent. As a consequence, the shape of response curves generated for a selected input feature (referred to as "active feature") might depend on the values of the other input features (referred to as "passive features"). In this work, we consider the case of systems whose response is approximated using regression neural networks. We propose to use counterfactual explanations (CFEs) for the identification of the features with the highest relevance on the shape of response curves generated by neural network black boxes. CFEs are generated by a genetic algorithm-based approach that solves a multi-objective optimization problem. In particular, given a response curve generated for an active feature, a CFE finds the minimum combination of passive features that need to be modified to alter the shape of the response curve. We tested our method on a synthetic dataset with 1-D inputs and two crop yield prediction datasets with 2-D inputs. The relevance ranking of features and feature combinations obtained on the synthetic dataset coincided with the analysis of the equation that was used to generate the problem. Results obtained on the yield prediction datasets revealed that the impact on fertilizer responsivity of passive features depends on the terrain characteristics of each field.
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