A Data-Mining Approach to Identification of Risk Factors in Safety Management Systems
提出一种数据挖掘方法，利用航空安全报告系统的文本数据，通过主题挖掘和数据流算法自动识别风险因素，准确率达76%至88%，有助于克服人工识别耗时和数据不完整的问题。
Incident reporting and investigation are components of safety management systems. Timely and accurate identification of risk factors is crucial to effective prevention strategies. However, risk factor identification is often hampered by size, complexity, and the need for human involvement in categorizing incident data. We present a data-mining approach to incident risk factor identification and analysis using data from the Aviation Safety Reporting System, which is part of the Federal Aviation Administration. Our approach is an attempt to overcome obstacles related to labor intensive manual identification of risk factors as well as incomplete data. First, topical mining techniques convert underused textual data (incident narratives) to serve as model input. Second, data-streaming algorithms are used to incrementally build and test classification models for risk factor identification. Three different classification algorithms were tested providing overall accuracy rates ranging from 76 percent to 88 percent, demonstrating the potential for effective use of large and unstructured incident data in safety management. Our research presents and demonstrates an approach to automated incident type identification and contributes to our understanding of the use of text-mining and data-streaming technologies in improving safety management systems.