Fair Adversarial Gradient Tree Boosting
Fair Adversarial Gradient Tree Boosting
复制标题
公平对抗梯度树提升
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Marcin Detyniecki
中科院分区：
文献类型：
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作者：
Vincent Grari;Boris Ruf;S. Lamprier;Marcin Detyniecki
公平分类已经成为机器学习研究中的一个重要课题。虽然大多数偏见缓解策略都集中在神经网络上，但我们注意到基于决策树的公平分类器缺乏工作，尽管它们已被证明非常有效。在表格数据中最先进的分类算法的最新比较中，树增强优于深度学习。为此，我们开发了一种对抗梯度树提升的新方法。该算法的目标是通过梯度树增强预测输出Y，同时最小化对抗神经网络预测敏感属性s的能力。该方法在每次迭代中将神经网络的梯度直接合并到梯度树增强中。我们在4个流行的数据集上对我们的方法进行了实证评估，并与最先进的算法进行了比较。结果表明，我们的算法在获得相同程度的公平性的情况下获得了更高的精度，使用了一组不同的通用公平性定义。
Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though they have proven very efficient. In an up-to-date comparison of state-of-the-art classification algorithms in tabular data, tree boosting outperforms deep learning. For this reason, we have developed a novel approach of adversarial gradient tree boosting. The objective of the algorithm is to predict the output Y with gradient tree boosting while minimizing the ability of an adversarial neural network to predict the sensitive attribute S. The approach incorporates at each iteration the gradient of the neural network directly in the gradient tree boosting. We empirically assess our approach on 4 popular data sets and compare against state-of-the-art algorithms. The results show that our algorithm achieves a higher accuracy while obtaining the same level of fairness, as measured using a set of different common fairness definitions.