基于AIS的海事异常检测中无监督学习的新型评估指标：MADQI
A Novel Evaluation Metric for Unsupervised Learning in AIS-Based Maritime Anomaly Detection: MADQI
AI总结：
提出一种无需标签数据的海事异常检测质量指标MADQI，通过结合四个子指标来评估无监督学习模型的异常检测性能。
摘要：
本文介绍了一个新的系统框架，用于检测海事自动识别系统（AIS）数据集中的异常。这些异常包括与速度、位置跳跃、时间间隔和转向角度相关的异常船舶行为。尽管诸如孤立森林之类的无监督学习算法被广泛用于检测异常船舶运动，但它们通常缺乏系统且有意义的评估措施。为了解决这一局限性，我们提出了一种称为海事异常检测质量指标（MADQI）的新型质量指标。所提出的MADQI是一个复合指标，旨在评估机器学习模型的异常检测性能，而无需标记数据。该框架使用哈弗辛距离计算来分析AIS数据集，并根据空间和行为特征识别异常。所提出的MADQI评估框架整合了四个相互关联的指标：异常率一致性（ARC）、物理合理性评分（PPS）、评分分布分离度（SDS）和极端案例证据（ECE）。这些指标通过使用多块评估和自适应缩放技术的自动归一化进行组合。在AIS数据集上的实验结果表明，所提出的框架实现了80.37%的MADQI分数，证明了其在无监督异常检测中的有效性。特别是，该算法在识别异常船舶行为方面表现强劲。在MADQI的各个组成部分中，ECE和ARC分别达到了0.907和1.000的分数，表明其在检测极端异常和保持异常率一致性方面具有出色的能力。总体而言，这些结果令人鼓舞，并表明所提出的框架为评估海事AIS数据中的无监督异常检测提供了一种可靠且有意义的方法。
英文摘要：
This paper introduces a new systematic framework for detecting anomalies in maritime Automatic Identification System (AIS) datasets. These anomalies include abnormal vessel behaviours related to speed, position jumps, time gaps, and turn angles. Although unsupervised learning algorithms such as Isolation Forest are widely used for detecting anomalous vessel movements, they often lack systematic and meaningful evaluation measures. To address this limitation, we propose a novel quality metric called Maritime Anomaly Detection Quality Index (MADQI). The prosed MADQI is a composite index designed to evaluate the anomaly detection performance of machine learning models without requiring labelled data. The proposed framework uses Haversine distance calculations to analyse AIS datasets and identify anomalies based on their spatial and behavioural characteristics. The proposed MADQI evaluation framework integrates four interconnected metrics: Anomaly Rate Consistency (ARC), Physical Plausibility Score (PPS), Score Distribution Separation (SDS), and Extreme Case Evidence (ECE). These metrics are combined through automatic normalisation using multi-chunk evaluation and adaptive scaling techniques. Experimental results on the AIS dataset show that the proposed framework achieved a MADQI score of 80.37%, demonstrating its effectiveness for unsupervised anomaly detection. In particular, the algorithm performed strongly in identifying abnormal vessel behaviour. Among the individual MADQI components, ECE and ARC achieved scores of 0.907 and 1.000, respectively, indicating excellent capability in detecting extreme anomalies and maintaining anomaly rate consistency. Overall, these results are encouraging and demonstrate that the proposed framework provides a reliable and meaningful approach for evaluating unsupervised anomaly detection in maritime AIS data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
