Human-in-the-Loop: Visual Analytics for Building Models Recognizing Behavioral Patterns in Time Series

作者：N. Andrienko, G. Andrienko, Alexander Artikis, Periklis Mantenoglou, Salvatore Rinzivillo · 发表于：IEEE Computer Graphics and Applications · 年份：2024 · DOI：10.1109/mcg.2024.3379851 · 被引用次数：10 · 研究领域：Medicine、Computer Science

Detecting complex behavioral patterns in temporal data, such as moving object trajectories, often relies on precise formal specifications derived from vague domain concepts. However, such methods are sensitive to noise and minor fluctuations, leading to missed pattern occurrences. Conversely, machine learning (ML) approaches require abundant labeled examples, posing practical challenges. Our visual analytics approach enables domain experts to derive, test, and combine interval-based features to discriminate patterns and generate training data for ML algorithms. Visual aids enhance recognition and characterization of expected patterns and discovery of unexpected ones. Case studies demonstrate feasibility and effectiveness of the approach, which offers a novel framework for integrating human expertise and analytical reasoning with ML techniques, advancing data analytics.