Sequential Cost-Sensitive Feature Acquisition

TL;DR

This paper introduces a reinforcement learning framework for cost-sensitive feature acquisition that adaptively selects features based on their costs, optimizing prediction accuracy within a budget.

Contribution

It combines representation learning with reinforcement learning to enable cost-aware, adaptive feature acquisition for efficient prediction.

Findings

Effective in reducing feature acquisition costs

Works well across various datasets and cost settings

Outperforms baseline methods in sparse prediction tasks

Abstract

We propose a reinforcement learning based approach to tackle the cost-sensitive learning problem where each input feature has a specific cost. The acquisition process is handled through a stochastic policy which allows features to be acquired in an adaptive way. The general architecture of our approach relies on representation learning to enable performing prediction on any partially observed sample, whatever the set of its observed features are. The resulting model is an original mix of representation learning and of reinforcement learning ideas. It is learned with policy gradient techniques to minimize a budgeted inference cost. We demonstrate the effectiveness of our proposed method with several experiments on a variety of datasets for the sparse prediction problem where all features have the same cost, but also for some cost-sensitive settings.