论文标题
使用Monte Carlo Tree搜索获取功能
Feature Acquisition using Monte Carlo Tree Search
论文作者
论文摘要
功能采集算法解决了获取信息功能的问题，同时平衡了获取成本以改善ML模型的学习性能。先前的方法重点是计算特征的预期效用值以确定采集序列。其他方法将问题作为马尔可夫决策过程（MDP）和基于增强学习算法的算法。与以前的方法相比，我们重点介绍1）将功能采集问题提出为MDP并应用蒙特卡洛树搜索，2）根据模型改进和获取成本计算每个采集步骤中的中介奖励，以及3）同时优化模型改进和通过多目标Monte Monte Carlo Carlo Carlo Tree搜索。通过近端政策优化和深层Q-Network算法作为基准，我们通过实验研究表明了我们提出的方法的有效性。
Feature acquisition algorithms address the problem of acquiring informative features while balancing the costs of acquisition to improve the learning performances of ML models. Previous approaches have focused on calculating the expected utility values of features to determine the acquisition sequences. Other approaches formulated the problem as a Markov Decision Process (MDP) and applied reinforcement learning based algorithms. In comparison to previous approaches, we focus on 1) formulating the feature acquisition problem as a MDP and applying Monte Carlo Tree Search, 2) calculating the intermediary rewards for each acquisition step based on model improvements and acquisition costs and 3) simultaneously optimizing model improvement and acquisition costs with multi-objective Monte Carlo Tree Search. With Proximal Policy Optimization and Deep Q-Network algorithms as benchmark, we show the effectiveness of our proposed approach with experimental study.
