Classification models for sequential flight test results for selecting air force pilot trainee

So Young Sohn, Yong Kwan Jo, Seong Ok Choy

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The main purpose of this paper is to present the selection criteria for Air Force pilot training candidates in order to save costs involved in the three stage sequential pilot training procedures currently used in Korea. We use classification models such as decision tree, logistic regression and neural network based on the aptitude test results of 288 ROK Air Force applicants in 1994-1996. Various classification models are compared in terms of classification accuracy, Receiver Operating Characteristic chart and Lift chart. As a result, neural network is evaluated as the best classification model for each sequential flight performance while logistic regression model outperforms the others for the overall flight result. The fitted logistic regression indicates that the factors such as attention sharing, instrument reading, and mechanical comprehension having significant effects on the flight results. We expect that the use of such classification models can increase the effectiveness of flight resources.

Original languageEnglish
Pages (from-to)591-599
Number of pages9
JournalExpert Systems with Applications
Volume26
Issue number4
DOIs
Publication statusPublished - 2004 May 1

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Logistics
Air
Neural networks
Flight dynamics
Decision trees
Costs

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

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Classification models for sequential flight test results for selecting air force pilot trainee. / Sohn, So Young; Jo, Yong Kwan; Choy, Seong Ok.

In: Expert Systems with Applications, Vol. 26, No. 4, 01.05.2004, p. 591-599.

Research output: Contribution to journalArticle

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