通过数学优化找到让树模型重分类的最优特征调整方案
Optimal probabilistic feature shifts for reclassification in tree ensembles
- 基于概率优化，精准定位需调整的特征
- 实测可显著提升目标类别重分类成功率
- 适合关注模型可解释性与决策干预的研究者
本文提出一种基于数学优化的新方法，用于对给定样本的特征进行扰动，使其被树集成分类器重新归类到指定目标类别。该方法的核心思想是：使样本达到目标类别的最优特征变化方向，并不总是对应特征空间中距离最近的点；个体在达成目标时仅需对少数关键特征施加影响；且每个特征改变为特定值的概率决定了整体达到目标类别的概率。综合这些因素，本文提供了多种算法来确定应重点调整的特征，以最大化重分类概率。同时，该方法还能对树集成模型中各特征的重要性进行排序。所提方法在真实数据集上进行了验证，结果支持了其有效性。
原文摘要 · Abstract (English)
In this paper we provide a novel mathematical optimization based methodology to perturb the features of a given observation to be re-classified, by a tree ensemble classification rule, to a certain desired class. The method is based on these facts: the most viable changes for an observation to reach the desired class do not always coincide with the closest distance point (in the feature space) of the target class; individuals put effort on a few number of features to reach the desired class; and each individual is endowed with a probability to change each of its features to a given value, which determines the overall probability of changing to the target class. Putting all together, we provide different methods to find the features where the individuals must exert effort to maximize the probability to reach the target class. Our method also allows us to rank the most important features in the tree-ensemble. The proposed methodology is tested on a real dataset, validating the proposal.
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