Test-Cost Sensitive Methods for Identifying Nearby Points
This work addresses a domain-specific challenge in data acquisition for applications with missing values, but it is incremental as it extends test-cost sensitive methods from classification to a related problem.
The paper tackles the problem of identifying nearby points from a large set when features are missing and costly to acquire, presenting tree-based and deep reinforcement learning models that outperform random agents on five real-world datasets.
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.