Classifier evasion consists in finding for a given instance $x$ the nearest
instance $x'$ such that the classifier predictions of $x$ and $x'$ are
different. We present two novel algorithms for systematically computing
evasions for tree ensembles such as boosted trees and random forests. Our first
algorithm uses a Mixed Integer Linear Program solver and finds the optimal
evading instance under an expressive set of constraints. Our second algorithm
trades off optimality for speed by using symbolic prediction, a novel algorithm
for fast finite differences on tree ensembles. On a digit recognition task, we
demonstrate that both gradient boosted trees and random forests are extremely
susceptible to evasions. Finally, we harden a boosted tree model without loss
of predictive accuracy by augmenting the training set of each boosting round
with evading instances, a technique we call adversarial boosting.

本研究提出针对树集合算法的两种新型分类器干扰算法，第一种算法使用 Mixed Integer Linear Program 求解器寻找在一定约束条件下的最优干扰实例，第二种算法通过基于符号预测的快速有限差分方法在速度和准确性之间进行权衡。在数字识别任务中，该研究证明了提升树和随机森林算法极易受到干扰，并通过增加干扰实例的训练集来提高提升树的鲁棒性，不会对其分类准确性造成影响。

树集成分类器的躲避和加固

Evasion and Hardening of Tree Ensemble Classifiers

Tree ensembles such as random forests and boosted trees are accurate but
difficult to understand, debug and deploy. In this work, we provide the inTrees
(interpretable trees) framework that extracts, measures, prunes and selects
rules from a tree ensemble, and calculates frequent variable interactions. An
rule-based learner, referred to as the simplified tree ensemble learner (STEL),
can also be formed and used for future prediction. The inTrees framework can
applied to both classification and regression problems, and is applicable to
many types of tree ensembles, e.g., random forests, regularized random forests,
and boosted trees. We implemented the inTrees algorithms in the "inTrees" R
package.

本文提供 inTrees 框架，通过提取、衡量、修剪和选择树集合中的规则，并计算频繁的变量交互来提高树集合的可解释性。inTrees 框架可以应用于分类和回归问题，并适用于多种树集合类型，例如随机森林、正则化随机森林和 Boosted Trees。

用 inTrees 解释树集合

Interpreting Tree Ensembles with inTrees

Besides serving as prediction models, classification trees are useful for
finding important predictor variables and identifying interesting subgroups in
the data. These functions can be compromised by weak split selection algorithms
that have variable selection biases or that fail to search beyond local main
effects at each node of the tree. The resulting models may include many
irrelevant variables or select too few of the important ones. Either
eventuality can lead to erroneous conclusions. Four techniques to improve the
precision of the models are proposed and their effectiveness compared with that
of other algorithms, including tree ensembles, on real and simulated data sets.

提出了四种改善模型精度的技术，在真实和模拟数据集上与树集成等其他算法进行对比，以提高分类树中选择关键预测变量和识别有趣的子组的能力。