Patterns (May 2024)
Unveiling value patterns via deep reinforcement learning in heterogeneous data analytics
Abstract
Summary: Artificial intelligence has substantially improved the efficiency of data utilization across various sectors. However, the insufficient filtering of low-quality data poses challenges to uncertainty management, threatening system stability. In this study, we introduce a data-valuation approach employing deep reinforcement learning to elucidate the value patterns in data-driven tasks. By strategically optimizing with iterative sampling and feedback, our method is effective in diverse scenarios and consistently outperforms the classic methods in both accuracy and efficiency. In China’s wind-power prediction, excluding 25% of the overall dataset deemed low-value led to a 10.5% improvement in accuracy. Utilizing just 42.8% of the dataset, the model discerned 80% of linear patterns, showcasing the data’s intrinsic and transferable value. A nationwide analysis identified a data-value-sensitive geographic belt across 10 provinces, leading to robust policy recommendations informed by variances in power outputs and data values, as well as geographic climate factors. The bigger picture: In the era of big data, the surge in volume is matched by the challenges of data quality, which resonates across all domains of data-driven and artificial-intelligence-related technologies. This research proposes a method to navigate these challenges by introducing a paradigm based on deep reinforcement learning, capable of discerning value in data across varied contexts. By enabling the strategic selection of optimal data samples, we envision a future where analytics are not just smarter but also more adaptable, allowing decision makers to harness the full potential of their data assets. The implications of this work extend beyond technical realms, offering insights that could shape policies and provide fresh perspectives in data-driven industries.
