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Cited by 10Explainable Artificial Intelligence (XAI)Data Mining and Machine Learning ApplicationsCustomer Service Quality and Loyalty

The DOI matches its Crossref record.

Explainable Stacking Machine Learning Ensemble for Predicting Airline Customer Satisfaction

Pranav, R., Gururaja, H. S.·2023·Lecture Notes in Networks and Systems
DOI 10.1007/978-981-19-9225-4_4
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Crossref · 10.1007/978-981-19-9225-4_4

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  • ML algorithms can be used to identify patterns in cargo data, such as shipping routes, handling times, and delivery destinations, which can be used to optimize cargo handling processes.

    “ML algorithms can be used to identify patterns in cargo data, such as shipping routes, handling times, and delivery destinations, which can be used to optimize cargo handling processes [113] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [113]

Citations

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Research works printed but not linked: 26

  1. Bellizzi MG, Eboli L, Mazzulla G (2020) Air transport service quality factors: a systematic literature review. Transp Res Procedia 45:218–225 · DOI
  2. Tegar K, Lestari A, Pratiwi SW (2017) An analysis of airlines customer satisfaction by improving customer service performance. In: Global research on sustainable transport (GROST 2017). Atlantis Press, pp 619–628
  3. García V (2019) Predicting airline customer satisfaction using k-nn ensemble regression models. Instituto de Ingeniería y Tecnología · DOI
  4. Monmousseau P, Marzuoli A, Feron E, Delahaye D (2020) Impact of Covid-19 on passengers and airlines from passenger measurements: managing customer satisfaction while putting the US Air transportation system to sleep. Transp Res Interdisc Perspect 7:100179
  5. Daudt F, Cinalli D, Garcia ACB (2021) Research on explainable artificial intelligence techniques: an user perspective. In: 2021 IEEE 24th international conference on computer supported cooperative work in design (CSCWD). IEEE, pp 144–149 · DOI
  6. Carta SM, Consoli S, Piras L, Podda AS, Recupero DR (2021) Explainable machine learning exploiting news and domain-specific lexicon for stock market forecasting. IEEE Access 9:30193–30205 · DOI
  7. An M, Noh Y (2009) Airline customer satisfaction and loyalty: impact of in-flight service quality. Serv Bus 3(3):293–307 · DOI
  8. Kumar S, Zymbler M (2019) A machine learning approach to analyze customer satisfaction from airline tweets. J Big Data 6(1):1–16 · DOI
  9. Baydogan C, Alatas B (2019) Detection of customer satisfaction on unbalanced and multi-class data using machine learning algorithms. In: 2019 1st International Informatics and Software Engineering Conference (UBMYK). IEEE, pp 1–5 · DOI
  10. Tan C (2021) Bidirectional LSTM model in predicting satisfaction level of passengers on airline service. In: 2021 2nd International conference on artificial intelligence and computer engineering (ICAICE). IEEE, pp 525–531 · DOI
  11. Shu Z (2021) Analysis of flight delay and cancellation prediction based on machine learning models. In: 2021 3rd International conference on machine learning, big data and business intelligence (MLBDBI). IEEE, pp 260–267 · DOI
  12. Jiang X, Zhang Y, Li Y, Zhang B (2022) Forecast and analysis of aircraft passenger satisfaction based on RF-RFE-LR model. Sci Rep 12(1):1–15 · DOI
  13. Hong SH, Kim B, Jung YG (2020) Correlation analysis of airline customer satisfaction using random forest with deep neural network and support vector machine model. Int J Internet Broadcast Commun 12(4):26–32
  14. Berliana AU, Bustamam A (2020) Implementation of stacking ensemble learning for classification of COVID-19 using image dataset CT scan and lung X-Ray. In: 2020 3rd International conference on information and communications technology (ICOIACT). IEEE, pp 148–152 · DOI
  15. Zhou A, Ren K, Li X, Zhang W (2019) MMSE: a multi-model stacking ensemble learning algorithm for purchase prediction. In: 2019 IEEE 8th joint international information technology and artificial intelligence conference (ITAIC). IEEE, pp 96–102 · DOI
  16. Liu J, Fan L, Jia Q, Wen L, Shi C (2021) Early diabetes prediction based on stacking ensemble learning model. In: 2021 33rd Chinese control and decision conference (CCDC). IEEE, pp 2687–2692 · DOI
  17. Fang T, Huang S, Zhou Y, Zhang H (2021) Multi-model stacking ensemble learning for student achievement prediction. In: 2021 12th International symposium on parallel architectures, algorithms and programming (PAAP). IEEE, pp 136–140 · DOI
  18. Li Z, Cai D, Wang J, Fu J, Qin L, Fu D (2020) A stacking ensemble learning model for mobile traffic prediction. In: 2020 IEEE/CIC international conference on communications in China (ICCC). IEEE, pp 542–547 · DOI
  19. Sahay S, Omare N, Shukla KK (2021) An approach to identify captioning keywords in an image using LIME. In: 2021 International conference on computing, communication, and intelligent systems (ICCCIS). IEEE, pp 648–651 · DOI
  20. Zou L, Goh HL, Liew CJY, Quah JL, Gu GT, Chew JJ, Ta A et al. (2022) Ensemble image explainable AI (XAI) algorithm for severe community-acquired pneumonia and COVID-19 respiratory infections. IEEE Trans Artif Intell · DOI
  21. Baniecki H, Kretowicz W, Piatyszek P, Wisniewski J, Biecek P (2021) Dalex: responsible machine learning with interactive explainability and fairness in python. J Mach Learn Res 22(1):9759–9765
  22. Yang Z, Li D (2019) Application of logistic regression with filter in data classification. In: 2019 Chinese control conference (CCC). IEEE, pp 3755–3759 · DOI
  23. Shaikhina T, Lowe D, Daga S, Briggs D, Higgins R, Khovanova N (2019) Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation. Biomed Signal Process Control 52:456–462 · DOI
  24. Breiman L (2001) Random forests. Mach Learn 45(1):5–32 · DOI · Paper page
  25. Cherif IL, Kortebi A (2019) On using extreme gradient boosting (XGBoost) machine learning algorithm for home network traffic classification. In: 2019 Wireless days (WD). IEEE, pp 1–6 · DOI
  26. Srinath T, Gururaja HS (2022) Explainable machine learning in identifying credit card defaulters. Global Transitions Proc · DOI

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Metadata

Authors
Pranav, R., & Gururaja, H. S.
Affiliations
B.M.S. College of Engineering (IN)
Corresponding author
Pranav, R.
ORCID
Gururaja, H. S.
Venue
Lecture Notes in Networks and Systems
Publisher
Springer Nature Singapore
Published
2023
Pages
41-56
ISSN
  • 2367-3370 (ISSN-L)
  • 2367-3389
DOI
10.1007/978-981-19-9225-4_4
OpenAlex
W4360981792
Cited by
10
References
0
Citations by year
2025: 5 · 2024: 2 · 2023: 3
FWCI
1.59
Type
conference paper
Language
en
Open access
closed
Licence
Locations
Lecture notes in networks and systems (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
  • Social Sciences › Business, Management and Accounting › Marketing › Customer Service Quality and Loyalty
Keywords
airline customer satisfaction, stacking ensemble, explainable AI, XGBoost, logistic regression, random forest, decision tree, black box problem, aircraft data, customer experience

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