Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41280

Identification of Human Factors in Aviation Incidents Using a Data Stream Approach

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Title: Identification of Human Factors in Aviation Incidents Using a Data Stream Approach
Authors: Shi, Donghui
Zurada, Jozef
Guan, Jian
Keywords: Aviation safety
Classification
Data stream
Holdout and Prequential Measures
Human factors
Issue Date: 04 Jan 2017
Abstract: This paper investigates the use of data streaming analytics to better predict the presence of human factors in aviation incidents with new incident reports. As new incidents data become available, the fresh information can help not only evaluate but also improve existing models. First, we use four algorithms in batch learning to establish a baseline for comparison purposes. These are NaiveBayes (NB), Cost Sensitive Classifier (CSC), Hoeffdingtree (VFDT), and OzabagADWIN (OBA). The traditional measure of the classification accuracy rate is used to test their performance. The results show that among the four, NB and CSC are the best classification algorithms. Then we test the classifiers in a data stream setting. The two performance measure methods Holdout and Interleaved Test-Then-Train or Prequential are used in this setting. The Kappa statistic charts of Prequential measure with a sliding window show that NB exhibits the best performance, and is better than the other algorithms. The two different measure methods, batch learning with 10-fold cross validation and data stream with Prequential measure, get one consistent result. CSC is a suitable for unbalanced data in batch learning, but it is not best in Kappa statistic for data stream. Valid incremental algorithms need to be developed for the data stream with unbalanced labels.
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/41280
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.127
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Business Intelligence, Analytics and Cognitive: Case Studies and Applications (COGS) Minitrack



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