Feedback-Based Dynamic Feature Selection for Constrained Continuous Data Acquisition

TL;DR

This paper introduces a feedback-driven dynamic feature selection method for efficient data acquisition in systems with many sensors, improving data relevance and quality for machine learning tasks.

Contribution

It formulates feature selection as a Markov Decision Process and employs a feedback-based algorithm with exploration, advancing data collection strategies in constrained environments.

Findings

Outperforms constrained baseline methods in feature selection.

Matches performance of unconstrained methods.

Efficiently selects relevant features in dynamic systems.

Abstract

Relevant and high-quality data are critical to successful development of machine learning applications. For machine learning applications on dynamic systems equipped with a large number of sensors, such as connected vehicles and robots, how to find relevant and high-quality data features in an efficient way is a challenging problem. In this work, we address the problem of feature selection in constrained continuous data acquisition. We propose a feedback-based dynamic feature selection algorithm that efficiently decides on the feature set for data collection from a dynamic system in a step-wise manner. We formulate the sequential feature selection procedure as a Markov Decision Process. The machine learning model performance feedback with an exploration component is used as the reward function in an $\epsilon$-greedy action selection. Our evaluation shows that the proposed feedback-based feature selection algorithm has superior performance over constrained baseline methods and matching performance with unconstrained baseline methods.