Test-Cost Sensitive Methods for Identifying Nearby Points

TL;DR

This paper introduces two test-cost sensitive models, a tree-based and a deep reinforcement learning approach, to efficiently identify nearby points with missing feature data, outperforming random strategies on real-world datasets.

Contribution

It proposes novel test-cost sensitive methods for nearby point identification, combining tree-based and deep reinforcement learning models, addressing a less-studied problem in this domain.

Findings

Models outperform random agents on real-world datasets.

Deep reinforcement learning effectively handles missing feature data.

Tree-based model provides a competitive alternative.

Abstract

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.