论文标题
用于识别附近点的测试成本敏感方法
Test-Cost Sensitive Methods for Identifying Nearby Points
论文作者
论文摘要
涉及缺失值的现实世界应用通常受到获取数据的成本的限制。测试成本敏感或昂贵的功能还考虑了获取功能的成本。在分类问题中已经对这种方法进行了广泛的研究。在本文中，我们研究了一个相关的测试成本敏感方法的问题，可以从大集合中识别附近点，鉴于具有一些未知特征值的新点。我们提出了两种模型，一种基于一棵树，另一个基于深厚的增强学习。在我们的模拟中，我们显示模型在一组五个现实世界数据集上的表现胜过随机代理。
Real-world applications that involve missing values are often constrained by the cost to obtain data. Test-cost sensitive, or costly feature, methods additionally consider the cost of acquiring features. Such methods have been extensively studied in the problem of classification. In this paper, we study a related problem of test-cost sensitive methods to identify nearby points from a large set, given a new point with some unknown feature values. We present two models, one based on a tree and another based on Deep Reinforcement Learning. In our simulations, we show that the models outperform random agents on a set of five real-world data sets.
