• Aug 09, 2018 News![CFP] The annual meeting of IJFCC Editorial Board, ICCTD 2019, will be held in Prague, Czech Republic during March 2-4, 2019.   [Click]
  • Aug 09, 2018 News!IJFCC Vol. 6, No. 1-No. 3 has been indexed by EI (Inspec).   [Click]
  • Dec 24, 2018 News!The papers published in Vol.7, No.1-No.2 have all received dois from Crossref.
General Information
    • ISSN: 2010-3751
    • Frequency: Bimonthly (2012-2016); Quarterly (Since 2017)
    • DOI: 10.18178/IJFCC
    • Editor-in-Chief: Prof. Mohamed Othman
    • Executive Editor: Ms. Cherry L. Chan
    • Abstracting/ Indexing: Google Scholar,  Crossref, Electronic Journals LibraryEI (INSPEC, IET), etc.
    • E-mail:  ijfcc@ejournal.net 
Prof. Mohamed Othman
Department of Communication Technology and Network Universiti Putra Malaysia, Malaysia
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.
IJFCC 2017 Vol.6(1): 21-26 ISSN: 2010-3751
doi: 10.18178/ijfcc.2017.6.1.482

Using Associative Petri Net with Over-Sampling Techniques to Construct an Aviation Incident Prediction Model

Hsiu-Sen Chiang, Tsung-Che Hsieh, Chia-Chen Chen, Neil Y. Yen, and Mu-Yen Chen
Abstract—Most current research applying data mining techniques to aviation safety focus on the acquisition and selection of data attribute fields, while the selection and analysis of classification algorithms has been largely overlooked. This study seeks to address this gap as a means of identifying key factors which result in aviation fatalities. The study data is taken from the Accident/Incident Data System (AIDS) flight accident database, which comprises an imbalanced dataset. The Synthetic Minority Oversampling Technique (SMOTE) is used to minimize the impact of data imbalance on classification results for categories with small datasets. Feature attribute selection was implemented through information gain (IG). This study also develops an Associative Petri Net (APN) model for comparison against five other classification methods: which are Naive Bayes, BayesNet, Support Vector Machine (SVM), Decision Tree (C4.5), and Radial Basis Function Network (RBFN). IG is used to identify 10 important factors: Event City, Event State, Flight Phase, Aircraft Model, Aircraft Series, Operator, Primary Flight Type, Flight Conduct Code, Flight Plan Filed Code, and Nbr of Engines. Results show that our proposed APN model had the highest overall accuracy rate, F-measure score, G-mean score among all algorithms. APN is based on the Apriori algorithm to provide a rule-based concept. Thus using the APN algorithm to build the model could produce an expert system for flight accident prediction.

Index Terms—Aviation safety, attribute selection, imbalanced dataset, SMOTE, associative petri net.

H. S. Chiang, T. C. Hsieh, and M. Y. Chen are with the National Taichung University of Science and Technology, Taichung, 40444 Taiwan (e-mail: hschiang@nutc.edu.tw, s4851798@gmail.com, mychen@nutc.edu.tw).
C. C. Chen is with National Chung Hsing University, Taichung, 402 Taiwan (e-mail: emily@nchu.edu.tw).
Neil Y. Yen is with University of Aizu, Aizu-Wakamatsu, Fukushima Pref. 965-8580 Japan (e-mail: neil219@gmail.com).


Cite: Hsiu-Sen Chiang, Tsung-Che Hsieh, Chia-Chen Chen, Neil Y. Yen, and Mu-Yen Chen, "Using Associative Petri Net with Over-Sampling Techniques to Construct an Aviation Incident Prediction Model," International Journal of Future Computer and Communication vol. 6, no. 1, pp. 21-26, 2017.

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