Feedback-Based Dynamic Feature Selection for Constrained Continuous Data Acquisition

• URL: http://arxiv.org/abs/2011.05112v2
• Date: Mon, 22 Feb 2021 16:19:24 GMT
• Title: Feedback-Based Dynamic Feature Selection for Constrained Continuous Data Acquisition
• Authors: Alp Sahin and Xiangrui Zeng
• Abstract summary: 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. Our evaluation shows that the proposed feedback-based feature selection algorithm has superior performance over constrained baseline methods.
• Score: 6.947442090579469
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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