Classification with Costly Features using Deep Reinforcement Learning

2017-11-20 · Jaromír Janisch, Tomáš Pevný, Viliam Lisý

We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost. We revisit a former approach that has framed the problem as a sequential decision-making problem and solved it by Q-learning with a linear approximation, where individual actions are either requests for feature values or terminate the episode by providing a classification decision. On a set of eight problems, we demonstrate that by replacing the linear approximation with neural networks the approach becomes comparable to the state-of-the-art algorithms developed specifically for this problem. The approach is flexible, as it can be improved with any new reinforcement learning enhancement, it allows inclusion of pre-trained high-performance classifier, and unlike prior art, its performance is robust across all evaluated datasets.

📄 PDF Abstract BibTeX arXiv:1711.07364

Code (1)

jaromiru/cwcf 공식 구현 pytorch

Tasks

ClassificationClassification with Costly FeaturesDecision MakingDeep Reinforcement LearningGeneral ClassificationQ-Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Sequential Decision Making

Classification with Costly Features in Hierarchical Deep Sets

2019-11-20 · Jaromír Janisch, Tomáš Pevný, Viliam Lisý

Classification with Costly Features (CwCF) is a classification problem that includes the cost of features in the optimization criteria. Individually for each sample, its features are sequentially acquired to maximize acc…

Classification Classification with Costly Features Deep Reinforcement Learning General Classification+4

Classification with Costly Features as a Sequential Decision-Making Problem

2019-09-05 · Jaromír Janisch, Tomáš Pevný, Viliam Lisý

This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget. Inspired by real-world use-cases…

Classification Classification with Costly Features Decision Making Deep Reinforcement Learning+5

Test-Cost Sensitive Methods for Identifying Nearby Points

2020-10-04 · Seung Gyu Hyun, Christopher Leung

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 hav…

Deep Reinforcement Learning Missing Values reinforcement-learning Reinforcement Learning (RL)

Costly Features Classification using Monte Carlo Tree Search

2021-02-14 · Ziheng Chen, Jin Huang, Hongshik Ahn, Xin Ning

We consider the problem of costly feature classification, where we sequentially select the subset of features to make a balance between the classification error and the feature cost. In this paper, we first cast the task…

Classification General Classification Sensitivity

Evaluation of Active Feature Acquisition Methods for Static Feature Settings

2023-12-06 · Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi

Active feature acquisition (AFA) agents, crucial in domains like healthcare where acquiring features is often costly or harmful, determine the optimal set of features for a subsequent classification task. As deploying an…

Offline RL reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)