Abstract
There are massive numbers of aviation inspection reports collected each year in the USA. These reports record findings from aviation surveillance inspections as well as accident or incident investigations. The goal of this paper is to apply text classification to the mining of these reports, and to show that the text classification methodology can be a critical element of the aviation safety decision support system. The performances of several text classification models are evaluated in the context of mining aviation inspection reports. The evaluation is given in terms of misclassification rates. Further breakdowns of the misclassification rates and related findings from the dataset suggest ways for improving data quality and for gathering information which are more pertinent for filing inspection reports.
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Liu, R.Y., Madigan, D., Eyheramendy, S. (2002). Text Classification for Mining Massive Aviation Inspection Reports. In: Dodge, Y. (eds) Statistical Data Analysis Based on the L1-Norm and Related Methods. Statistics for Industry and Technology. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-8201-9_31
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DOI: https://doi.org/10.1007/978-3-0348-8201-9_31
Publisher Name: Birkhäuser, Basel
Print ISBN: 978-3-0348-9472-2
Online ISBN: 978-3-0348-8201-9
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