论文标题
深度学习的重要性排名
Feature Importance Ranking for Deep Learning
论文作者
论文摘要
特征重要性排名已成为可解释AI的强大工具。但是，其组合优化的性质为深度学习带来了巨大的挑战。在本文中，我们提出了一种新颖的双NET体系结构，该架构由操作员和选择器组成，用于发现固定大小的最佳特征子集，并同时在最佳子集中对这些特征的重要性进行排名。在学习过程中，通过选拔者生成的最佳功能子集候选者对操作员进行了监督的学习任务，该候选者学习了预测在不同最佳子集候选者上工作的操作员的学习绩效。我们开发了一种替代学习算法，该算法共同训练两个网，并将随机的本地搜索程序纳入学习以应对组合优化挑战。在部署中，选择器生成一个最佳特征子集并将特征的特征对，而操作员基于测试数据的最佳子集进行预测。对合成，基准和实际数据集的详尽评估表明，我们的方法的表现优于几个最新特征的重要性排名和监督功能选择方法。 （我们的源代码可用：https：//github.com/maksym33/featureimportancedl）
Feature importance ranking has become a powerful tool for explainable AI. However, its nature of combinatorial optimization poses a great challenge for deep learning. In this paper, we propose a novel dual-net architecture consisting of operator and selector for discovery of an optimal feature subset of a fixed size and ranking the importance of those features in the optimal subset simultaneously. During learning, the operator is trained for a supervised learning task via optimal feature subset candidates generated by the selector that learns predicting the learning performance of the operator working on different optimal subset candidates. We develop an alternate learning algorithm that trains two nets jointly and incorporates a stochastic local search procedure into learning to address the combinatorial optimization challenge. In deployment, the selector generates an optimal feature subset and ranks feature importance, while the operator makes predictions based on the optimal subset for test data. A thorough evaluation on synthetic, benchmark and real data sets suggests that our approach outperforms several state-of-the-art feature importance ranking and supervised feature selection methods. (Our source code is available: https://github.com/maksym33/FeatureImportanceDL)
