Araştırma Makalesi
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Yıl 2021, Cilt: 1 Sayı: 1, 8 - 19, 30.04.2021

Muhammed Tekin , Murat Gök

Öz

Kaynakça

  • 1. H. Akmeşe and S. Aras, “Otel İşletmelerinde Gelir Yönetimi Uygulamaları: İzmir’de Faaliyet Gösteren 4 ve 5 Yıldızlı Otel İşletmelerinde Bir Uygulama,” Int. J. Acad. Value Stud., vol. 3, no. 16, pp. 344–358, 2017, [Online]. Available: Https://www.researchgate.net/profile/Halil_Akmese/publication/321113419_Otel_Isletmelerinde_Gelir_Yonetimi_Uygulamalari_Izmir’de_Faaliyet_Gosteren_4_Ve/links/5a2322760f7e9b71dd053d40/Otel-IsletmelerindeGelir-Yoenetimi-Uygulamalari-Izmirde-Faaliyet-Goeste.
  • 2. J. C. H. Chen, “An overview of research on revenue management : current issues and future research WenChyuan Chiang * Xiaojing Xu,” Oper. Manag., 2007.
  • 3. N. Antonio, A. De Almeida, and L. Nunes, “An automated machine learning based decision support system to predict hotel booking cancellations,” Data Sci. J., 2019, doi: 10.5334/dsj-2019-032.
  • 4. R. Mehrotra and J. Ruttley, Revenue management. Washington, DC: American Hotel and Lodging Association,2006.
  • 5. G. Zöngür, K. G. Yilmaz, and A. Güngördü, “Konaklama İşletmelerinde Dış Kaynak Kullanımı: Ankara İlindeki Dört ve Beş Yıldızlı Otel İşletmelerinde Bir Uygulama,” Gazi Üniversitesi İktisadi ve İdari Bilim. Fakültesi Derg., 2016.
  • 6. G. J. van Ryzin and K. T. Talluri, “An introduction to revenue management. In Emerging Theory, Methods, and Applications,” INFORMS., 2005, doi: 10.1287/educ.1053.0019.
  • 7. D. Romero Morales and J. Wang, “Forecasting cancellation rates for services booking revenue management using data mining,” Eur. J. Oper. Res., 2010, doi: 10.1016/j.ejor.2009.06.006.
  • 8. P. Liu, Hotel demand/cancellation analysis and estimation of unconstrained demand using statistical methods. In: Yeoman. 2004, pp. 91–108.
  • 9. M. Boz, E. Canbazoğlu, Z. Özen, and S. Gülseçen, “Otel Rezervasyon İptallerinin Makine Öğrenmesi Yöntemleri ile Tahmin Edilmesi,” Veri Bilim., vol. 1, no. 1, pp. 7–14, Dec. 2018, Accessed: Apr. 18, 2020. [Online]. Available: http://dergipark.org.tr/en/pub/veri/issue/41532/490816#author994713.
  • 10. B. M. Noone and C. H. Lee, “Hotel overbooking: The effect of overcompensation on customers’ reactions to denied service,” J. Hosp. Tour. Res., 2011, doi: 10.1177/1096348010382238.
  • 11. V. İyitoğlu and N. Tetik, “Fazla Oda Satan Otellerin Kullandığı Yaygın İyileştirme Faaliyetinin Yerli Turistlerin Memnuniyet ve Tekrar Gelme Niyetlerine Etkisinin Bazı Değişkenler Açısından Değerlendirilmesi,” Tur. Akad. Derg., vol. 3, no. 1, pp. 57–68, May 2016, Accessed: Apr. 18, 2020. [Online]. Available: http://dergipark.org.tr/tr/pub/touraj/issue/24968/263496.
  • 12. S. J. Smith, H. G. Parsa, M. Bujisic, and J. P. van der Rest, “Hotel cancelation policies, distributive and procedural fairness, and consumer patronage: A study of the lodging industry,” J. Travel Tour. Mark., 2015, doi:10.1080/10548408.2015.1063864.
  • 13. W. Sullivan, Machine learning Beginners Guide Algorithms: Supervised & Unsupervised learning, Decision Tree & Random Forest Introduction. USA,: CreateSpace Independent Publishing Platform, 2017.
  • 14. S. Ivanov, Hotel Revenue Management: From Theory to Practice. 2014.
  • 15. D. K. Hayes and A. Miller, Revenue Management for the Hospitality Industry. Wiley, 2010.
  • 16. W. Caicedo-Torres and F. Payares, “A machine learning model for occupancy rates and demand forecasting in the hospitality industry,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, doi: 10.1007/978-3-319-47955-2_17.
  • 17. L. R. Weatherford and S. E. Kimes, “A comparison of forecasting methods for hotel revenue management,” Int. J. Forecast., 2003, doi: 10.1016/S0169-2070(02)00011-0.
  • 18. S. Pölt, “Forecasting is difficult – especially if it refers to the future,” in Reservations and Yield Management Study Group Annual Meeting Proceedings, 1998, doi: 10.1002/for.1094.
  • 19. H.-C. Huang, A. Chang, and C.-C. Ho, “Using Artificial Neural Networks to Establish a Customer-cancellation Prediction Model,” Prz. Elektrotechniczny, vol. 89, pp. 178–180, 2013.
  • 20. N. Antonio, A. de Almeida, and L. Nunes, “Predicting hotel booking cancellations to decrease uncertainty and increase revenue,” Tour. Manag. Stud., 2017, doi: 10.18089/tms.2017.13203.
  • 21. N. Antonio, A. de Almeida, and L. Nunes, “Hotel booking demand datasets,” Data Br., 2019, doi: 10.1016/j.dib.2018.11.126.
  • 22. “Python.” https://www.python.org/ (accessed Apr. 01, 2020).
  • 23. T. Elliott, “The State of the Octoverse: machine learning,” 2019. https://github.blog/2019-01-24-the-state-of-theoctoverse-machine-learning/ (accessed Apr. 19, 2020).
  • 24. P. Cunningham and S. J. Delany, “K -Nearest Neighbour Classifiers,” Mult. Classif. Syst., 2007, doi:10.1016/S0031-3203(00)00099-6.
  • 25. P. Hall, B. U. Park, and R. J. Samworth, “Choice of neighbor order in nearest-neighbor classification,” Ann. Stat., 2008, doi: 10.1214/07-AOS537.
  • 26. K. A. Heller, “Efficient Bayesian Methods for Clustering,” New York, 2007.
  • 27. S. Kilic, “Binary logistic regression analysis,” J. Mood Disord., 2015, doi: 10.5455/jmood.20151202122141.
  • 28. “Lojistik Regresyon Analizi: Tıp Verileri Üzerine Bir Uygulama,” Kocaeli Üniversitesi Sos. Bilim. Derg., 2004.
  • 29. S. Lek and Y. S. Park, “Artificial Neural Networks,” Encycl. Ecol., pp. 237–245, Jan. 2008, doi: 10.1016/B978-008045405-4.00173-7.
  • 30. K. A. Grajski, L. Breiman, G. V. Di Prisco, and W. J. Freeman, “Classification of EEG Spatial Patterns with a Tree-Structured Methodology: CART,” IEEE Trans. Biomed. Eng., 1986, doi: 10.1109/TBME.1986.325684.
  • 31. J. R. Quinlan, “Induction of Decision Trees,” Mach. Learn., 1986, doi: 10.1023/A:1022643204877.
  • 32. Y. Yang, S. S. Farid, and N. F. Thornhill, “Prediction of biopharmaceutical facility fit issues using decision tree analysis,” Comput. Aided Chem. Eng., vol. 32, pp. 61–66, Jan. 2013, doi: 10.1016/B978-0-444-63234-0.50011-
  • 33. E. Sezer, A. Bozkır, A. S. Yağız, and S. Gökçeoğlu, “Karar Ağacı Derinliğinin CART Algoritmasında Kestirim Kapasitesine Etkisi: Bir Tünel Açma Makinesinin İlerleme Hızı Üzerinde Uygulama,” in Akıllı Sistemlerde Yenilikler ve Uygulamaları Sempozyumu, 2010.
  • 34. Aslı ÇALIŞ, Sema KAYAPINAR, and Tahsin ÇETİNYOKUŞ, “Veri Madenciliğinde Karar Ağacı Algoritmaları ile Bilgisayar ve İnternet Güvenliği Üzerine Bir Uygulama,” Endüstri Mühendisliği Derg., 2014.
  • 35. T. K. Ho, “Random decision forests,” in Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 1995, doi: 10.1109/ICDAR.1995.598994.
  • 36. L. Breiman, “Random forests,” Mach. Learn., 2001, doi: 10.1023/A:1010933404324.
  • 37. J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Ann. Stat., 2001, doi: 10.2307/2699986.
  • 38. K. A. Ross et al., “Cross-Validation,” in Encyclopedia of Database Systems, Springer US, 2009, pp. 532–538.
  • 39. D. M. Hawkins, “The Problem of Overfitting,” Journal of Chemical Information and Computer Sciences, vol. 44, no. 1. pp. 1–12, Jan. 2004, doi: 10.1021/ci0342472.
  • 40. T.-T. Wong, “Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation,” Pattern Recognit., vol. 48, no. 9, pp. 2839–2846, Sep. 2015, doi: 10.1016/J.PATCOG.2015.03.009.
  • 41. “Çapraz doğrulama diyagramı” (Accessed Apr. 19, 2020). https://upload.wikimedia.org/wikipedia/commons/a/a6/Çapraz_doğrulama_diyagramı.svg
  • 42. J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” J. Mach. Learn. Res., 2012.
  • 43. G. Luo, “A review of automatic selection methods for machine learning algorithms and hyper-parameter values,” Netw. Model. Anal. Heal. Informatics Bioinforma., 2016, doi: 10.1007/s13721-016-0125-6.

Performance Comparison of Classification Algorithms in Hotel Booking Cancellation Prediction

Yıl 2021, Cilt: 1 Sayı: 1, 8 - 19, 30.04.2021

Muhammed Tekin , Murat Gök

Öz

Having a high occupancy rate is one of the most important goals of hotel management. However, booking cancellations have a negative effect on the profit rates of the hotels. Although hotel businesses try to develop various solutions to overcome this problem, they cannot achieve the desired result. In this context, it is of great importance for hotels to be able to predict booking cancellations that may occur.

In order to solve this problem, in this study, k-Nearest Neighbors algorithm, Logistic Regression, Artificial Neural Networks, Decision Tree algorithm, Random Forest algorithm and Gradient Boosting algorithm are run on an open shared dataset that includes the reservation information of various hotels between 2015 and 2017. When the results are compared, it has been shown that K-Nearest Neighbors and Random Forest algorithms are the best solutions to the problem with both have 85% accuracy.

Anahtar Kelimeler

hotel booking classification algorithms cancellation prediction

Kaynakça

  • 1. H. Akmeşe and S. Aras, “Otel İşletmelerinde Gelir Yönetimi Uygulamaları: İzmir’de Faaliyet Gösteren 4 ve 5 Yıldızlı Otel İşletmelerinde Bir Uygulama,” Int. J. Acad. Value Stud., vol. 3, no. 16, pp. 344–358, 2017, [Online]. Available: Https://www.researchgate.net/profile/Halil_Akmese/publication/321113419_Otel_Isletmelerinde_Gelir_Yonetimi_Uygulamalari_Izmir’de_Faaliyet_Gosteren_4_Ve/links/5a2322760f7e9b71dd053d40/Otel-IsletmelerindeGelir-Yoenetimi-Uygulamalari-Izmirde-Faaliyet-Goeste.
  • 2. J. C. H. Chen, “An overview of research on revenue management : current issues and future research WenChyuan Chiang * Xiaojing Xu,” Oper. Manag., 2007.
  • 3. N. Antonio, A. De Almeida, and L. Nunes, “An automated machine learning based decision support system to predict hotel booking cancellations,” Data Sci. J., 2019, doi: 10.5334/dsj-2019-032.
  • 4. R. Mehrotra and J. Ruttley, Revenue management. Washington, DC: American Hotel and Lodging Association,2006.
  • 5. G. Zöngür, K. G. Yilmaz, and A. Güngördü, “Konaklama İşletmelerinde Dış Kaynak Kullanımı: Ankara İlindeki Dört ve Beş Yıldızlı Otel İşletmelerinde Bir Uygulama,” Gazi Üniversitesi İktisadi ve İdari Bilim. Fakültesi Derg., 2016.
  • 6. G. J. van Ryzin and K. T. Talluri, “An introduction to revenue management. In Emerging Theory, Methods, and Applications,” INFORMS., 2005, doi: 10.1287/educ.1053.0019.
  • 7. D. Romero Morales and J. Wang, “Forecasting cancellation rates for services booking revenue management using data mining,” Eur. J. Oper. Res., 2010, doi: 10.1016/j.ejor.2009.06.006.
  • 8. P. Liu, Hotel demand/cancellation analysis and estimation of unconstrained demand using statistical methods. In: Yeoman. 2004, pp. 91–108.
  • 9. M. Boz, E. Canbazoğlu, Z. Özen, and S. Gülseçen, “Otel Rezervasyon İptallerinin Makine Öğrenmesi Yöntemleri ile Tahmin Edilmesi,” Veri Bilim., vol. 1, no. 1, pp. 7–14, Dec. 2018, Accessed: Apr. 18, 2020. [Online]. Available: http://dergipark.org.tr/en/pub/veri/issue/41532/490816#author994713.
  • 10. B. M. Noone and C. H. Lee, “Hotel overbooking: The effect of overcompensation on customers’ reactions to denied service,” J. Hosp. Tour. Res., 2011, doi: 10.1177/1096348010382238.
  • 11. V. İyitoğlu and N. Tetik, “Fazla Oda Satan Otellerin Kullandığı Yaygın İyileştirme Faaliyetinin Yerli Turistlerin Memnuniyet ve Tekrar Gelme Niyetlerine Etkisinin Bazı Değişkenler Açısından Değerlendirilmesi,” Tur. Akad. Derg., vol. 3, no. 1, pp. 57–68, May 2016, Accessed: Apr. 18, 2020. [Online]. Available: http://dergipark.org.tr/tr/pub/touraj/issue/24968/263496.
  • 12. S. J. Smith, H. G. Parsa, M. Bujisic, and J. P. van der Rest, “Hotel cancelation policies, distributive and procedural fairness, and consumer patronage: A study of the lodging industry,” J. Travel Tour. Mark., 2015, doi:10.1080/10548408.2015.1063864.
  • 13. W. Sullivan, Machine learning Beginners Guide Algorithms: Supervised & Unsupervised learning, Decision Tree & Random Forest Introduction. USA,: CreateSpace Independent Publishing Platform, 2017.
  • 14. S. Ivanov, Hotel Revenue Management: From Theory to Practice. 2014.
  • 15. D. K. Hayes and A. Miller, Revenue Management for the Hospitality Industry. Wiley, 2010.
  • 16. W. Caicedo-Torres and F. Payares, “A machine learning model for occupancy rates and demand forecasting in the hospitality industry,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, doi: 10.1007/978-3-319-47955-2_17.
  • 17. L. R. Weatherford and S. E. Kimes, “A comparison of forecasting methods for hotel revenue management,” Int. J. Forecast., 2003, doi: 10.1016/S0169-2070(02)00011-0.
  • 18. S. Pölt, “Forecasting is difficult – especially if it refers to the future,” in Reservations and Yield Management Study Group Annual Meeting Proceedings, 1998, doi: 10.1002/for.1094.
  • 19. H.-C. Huang, A. Chang, and C.-C. Ho, “Using Artificial Neural Networks to Establish a Customer-cancellation Prediction Model,” Prz. Elektrotechniczny, vol. 89, pp. 178–180, 2013.
  • 20. N. Antonio, A. de Almeida, and L. Nunes, “Predicting hotel booking cancellations to decrease uncertainty and increase revenue,” Tour. Manag. Stud., 2017, doi: 10.18089/tms.2017.13203.
  • 21. N. Antonio, A. de Almeida, and L. Nunes, “Hotel booking demand datasets,” Data Br., 2019, doi: 10.1016/j.dib.2018.11.126.
  • 22. “Python.” https://www.python.org/ (accessed Apr. 01, 2020).
  • 23. T. Elliott, “The State of the Octoverse: machine learning,” 2019. https://github.blog/2019-01-24-the-state-of-theoctoverse-machine-learning/ (accessed Apr. 19, 2020).
  • 24. P. Cunningham and S. J. Delany, “K -Nearest Neighbour Classifiers,” Mult. Classif. Syst., 2007, doi:10.1016/S0031-3203(00)00099-6.
  • 25. P. Hall, B. U. Park, and R. J. Samworth, “Choice of neighbor order in nearest-neighbor classification,” Ann. Stat., 2008, doi: 10.1214/07-AOS537.
  • 26. K. A. Heller, “Efficient Bayesian Methods for Clustering,” New York, 2007.
  • 27. S. Kilic, “Binary logistic regression analysis,” J. Mood Disord., 2015, doi: 10.5455/jmood.20151202122141.
  • 28. “Lojistik Regresyon Analizi: Tıp Verileri Üzerine Bir Uygulama,” Kocaeli Üniversitesi Sos. Bilim. Derg., 2004.
  • 29. S. Lek and Y. S. Park, “Artificial Neural Networks,” Encycl. Ecol., pp. 237–245, Jan. 2008, doi: 10.1016/B978-008045405-4.00173-7.
  • 30. K. A. Grajski, L. Breiman, G. V. Di Prisco, and W. J. Freeman, “Classification of EEG Spatial Patterns with a Tree-Structured Methodology: CART,” IEEE Trans. Biomed. Eng., 1986, doi: 10.1109/TBME.1986.325684.
  • 31. J. R. Quinlan, “Induction of Decision Trees,” Mach. Learn., 1986, doi: 10.1023/A:1022643204877.
  • 32. Y. Yang, S. S. Farid, and N. F. Thornhill, “Prediction of biopharmaceutical facility fit issues using decision tree analysis,” Comput. Aided Chem. Eng., vol. 32, pp. 61–66, Jan. 2013, doi: 10.1016/B978-0-444-63234-0.50011-
  • 33. E. Sezer, A. Bozkır, A. S. Yağız, and S. Gökçeoğlu, “Karar Ağacı Derinliğinin CART Algoritmasında Kestirim Kapasitesine Etkisi: Bir Tünel Açma Makinesinin İlerleme Hızı Üzerinde Uygulama,” in Akıllı Sistemlerde Yenilikler ve Uygulamaları Sempozyumu, 2010.
  • 34. Aslı ÇALIŞ, Sema KAYAPINAR, and Tahsin ÇETİNYOKUŞ, “Veri Madenciliğinde Karar Ağacı Algoritmaları ile Bilgisayar ve İnternet Güvenliği Üzerine Bir Uygulama,” Endüstri Mühendisliği Derg., 2014.
  • 35. T. K. Ho, “Random decision forests,” in Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 1995, doi: 10.1109/ICDAR.1995.598994.
  • 36. L. Breiman, “Random forests,” Mach. Learn., 2001, doi: 10.1023/A:1010933404324.
  • 37. J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Ann. Stat., 2001, doi: 10.2307/2699986.
  • 38. K. A. Ross et al., “Cross-Validation,” in Encyclopedia of Database Systems, Springer US, 2009, pp. 532–538.
  • 39. D. M. Hawkins, “The Problem of Overfitting,” Journal of Chemical Information and Computer Sciences, vol. 44, no. 1. pp. 1–12, Jan. 2004, doi: 10.1021/ci0342472.
  • 40. T.-T. Wong, “Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation,” Pattern Recognit., vol. 48, no. 9, pp. 2839–2846, Sep. 2015, doi: 10.1016/J.PATCOG.2015.03.009.
  • 41. “Çapraz doğrulama diyagramı” (Accessed Apr. 19, 2020). https://upload.wikimedia.org/wikipedia/commons/a/a6/Çapraz_doğrulama_diyagramı.svg
  • 42. J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” J. Mach. Learn. Res., 2012.
  • 43. G. Luo, “A review of automatic selection methods for machine learning algorithms and hyper-parameter values,” Netw. Model. Anal. Heal. Informatics Bioinforma., 2016, doi: 10.1007/s13721-016-0125-6.
Toplam 43 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Articles
Yazarlar

Muhammed Tekin

Murat Gök

Yayımlanma Tarihi 30 Nisan 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 1 Sayı: 1

Kaynak Göster

APA Tekin, M., & Gök, M. (2021). Performance Comparison of Classification Algorithms in Hotel Booking Cancellation Prediction. Artificial Intelligence Theory and Applications, 1(1), 8-19.
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