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Implementing the Perturbation Approach for Reliability Assessment: A Case Study in the Context of Flight Delay Prediction

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

Organizations employ prediction models as a foundation for decision-making. A prediction model learned from training data is often only evaluated using global quality indicators, e.g., accuracy and precision. These global indicators, however, do not provide guidance regarding the reliability of the prediction for a specific input case. In this paper, we instantiate a generic reference process for implementing reliability assessment methods for specific input cases on the real-world use case of flight delay prediction. We specifically implement the perturbation approach to reliability assessment for this use case and then describe the steps that were taken to train the prediction model, with an emphasis on the activities required to implement the perturbation approach. The perturbation approach consists of slightly altering feature values for an individual input case, e.g., within the margins of error of a sensed value, and observe whether the prediction of the model changes, which would render the prediction unreliable. The implementation of the perturbation approach requires decisions and documentations along the various stages of the data mining process. A generic tool can be used to document and perform reliability assessment using the perturbation approach.
OriginalspracheEnglisch
TitelProceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025), Porto, Portugal, April 4-6, 2025
UntertitelVolume 1, Part 1 of 2
Herausgeber*innenJoaquim Filipe, Michael Smialek, Alexander Brodsky, Slimane Hammoudi
VerlagCurran Associates, Inc.
Seiten75-86
Seitenumfang12
Auflage1
ISBN (elektronisch)9789897587498
ISBN (Print)979-8-3313-1876-5
DOIs
PublikationsstatusVeröffentlicht - Apr. 2025

Publikationsreihe

NameInternational Conference on Enterprise Information Systems, ICEIS - Proceedings
Band1
ISSN (elektronisch)2184-4992

Wissenschaftszweige

  • 102030 Semantische Technologien
  • 502050 Wirtschaftsinformatik
  • 102010 Datenbanksysteme
  • 102035 Data Science
  • 503008 E-Learning
  • 502058 Digitale Transformation
  • 509026 Digitalisierungsforschung
  • 102033 Data Mining
  • 102 Informatik
  • 102027 Web Engineering
  • 102028 Knowledge Engineering
  • 102016 IT-Sicherheit
  • 102015 Informationssysteme
  • 102025 Verteilte Systeme

JKU-Schwerpunkte

  • Digital Transformation

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