Research Article
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The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem

Year 2023, Volume: 7 Issue: 2, 360 - 383, 29.12.2023

Ali Alsaç Mehmet Mutlu Yenisey Murat Can Ganiz Mustafa Dağtekin Taner Ulusinan

https://doi.org/10.26650/acin.1394019

Abstract

Designing a prediction method with machine learning algorithms and increasing the prediction success is one of the most important research areas and aims of today. Models designed using classification algorithms are frequently used especially in problem types that require prediction. In this study, real life data is used to answer the question of which problem type should be included in the Information Technology Service Management (ITSM) system. An important step in the search for a solution is to examine the dataset with regularization methods. Experimental results have been obtained to establish the overfitting or underfitting balance of the dataset with L1 and L2 regularization methods. While the Root-Mean-Square Error (RMSE) value was approximately 0.13 in the regression model without regularization, this value was found to be approximately 0.083 after L1 regularization.With the regularized dataset, new results were obtained using Artificial Neural Network (ANN), Logistic Regression (LR), Support Vector Machine (SVM) classifier algorithms. SVM algorithm was the most successful model with a performance of approximately 0.73. It is followed by LR and ANN respectively. Accuracy, Precision, Recall and F1Score were used as evaluation metrics. It is seen that the use of regularization methods, especially in the preparation of real-life data for use in machine learning or other artificial intelligence research, will contribute to increasing the success level of the model.

Keywords

IT service management regularization prediction classification

Supporting Institution

TUBİTAK

Project Number

This work is supported by TUBITAK 1509 program number 9210017 and TUBITAK 2244 program number 119C056.

Thanks

I would like to greatly acknowledge TUBITAK and Experteam which is a trademark of Uzman Bilişim A.Ş.

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Sorun Türü Tahmini Probleminde Düzenlileştirme Yönteminin Model Başarısı Üzerindeki Etkisi

Year 2023, Volume: 7 Issue: 2, 360 - 383, 29.12.2023

Ali Alsaç Mehmet Mutlu Yenisey Murat Can Ganiz Mustafa Dağtekin Taner Ulusinan

https://doi.org/10.26650/acin.1394019

Abstract

Matematik düzleminde bir tahmin yöntemi tasarlamak ve başarılı sonuçlarından faydalanmak günümüzün önemli araştırma alanlarından ve amaçlarından biri olarak öne çıkmaktadır. Sınıflandırma algoritmaları kullanılarak tasarlanan modeller özellikle tahmin gerektiren problem türlerinde sıklıkla kullanılmaktadır. Çalışmada gerçek hayat verileri kullanılarak bir gerçek hayat problemi olan müşteriden gelen çözüm talebinin Bilgi Teknolojisi Hizmet Yönetimi (BTHY) sistemi içinde hangi sorun tipine dahil edilmesi gerektiği sorusuna cevap aranmaktadır. Çözüm arayışının önemli bir aşamasında veri kümesinin Regülarizasyon yöntemleri ile incelenmesi yer almaktadır. L1 ve L2 regülarizasyon yöntemleri ile veri kümesinin overfitting ya daunderfitting dengesinin kurulması için deneysel sonuçlar alınmıştır. Regülarizasyon uygulanmamış regresyon modelinde Kök Ortalama Kare Hatası (RMSE) değeri yaklaşık olarak 0,13 iken L1 regülarizasyonu sonucunda bu değer yaklaşık 0,083 olarak bulunmuştur. Düzenlileştirilmiş veri kümesi ile Yapay Sinir Ağları (YSA), Lojistik Regresyon (LR), Destek Vektör Makinaları (DVM) sınıflandırıcı algoritmaları kullanılarak yeni sonuçlar elde edilmiştir. DVM algoritması yaklaşık 0,73 başarım sonucu ile en başarılı model olmuştur. Sırasıyla LR ve YSA takip etmektedir. Değerlendirme metrikleri olarak Accuracy, Precision, Recall ve F1Score kullanılmıştır. Özellikle gerçek hayat verilerinin makina öğrenmesi ya da diğer yapay zeka araştırmalarında kullanımı için hazırlanması aşamasında Regülarizasyon yöntemlerinden faydalanmanın modelin başarı düzeyinin artmasında katkısı olacağı görülmektedir.

Keywords

Bilgi işlem servis yönetimi regülarizasyon tahmin sınıflandırma

Project Number

This work is supported by TUBITAK 1509 program number 9210017 and TUBITAK 2244 program number 119C056.

References

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  • Koçoğlu, F. Ö., & Özcan, T. (2022). A grid search optimized extreme learning machine approach for customer churn prediction. Journal of Engineering Research. google scholar
  • Kotsilieris, T., Anagnostopoulos, I., & Livieris, I. E. (2022). Special Issue: Regularization Techniques for Machine Learning and Their Appli-cations. In Electronics (Switzerland) (Vol. 11, Issue 4). https://doi.org/10.3390/electronics11040521 google scholar
  • Li, N., & Zhou, Z. H. (2009). Selective ensemble under regularization framework. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5519 LNCS. https://doi.org/10.1007/978-3-642-02326-2_30 google scholar
  • Mantovani, R. G., Horvath, T., Cerri, R., Vanschoren, J., & De Carvalho, A. C. P. L. F. (2017). Hyper-Parameter Tuning of a Decision Tree Induction Algorithm. Proceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016. https://doi.org/10.1109/BRACIS.2016.018 google scholar
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There are 51 citations in total.

Details

Primary Language English
Subjects Satisfiability and Optimisation, Modelling and Simulation
Journal Section Research Article
Authors

Ali Alsaç 0000-0002-8585-4501

Mehmet Mutlu Yenisey 0000-0002-4532-344X

Murat Can Ganiz 0000-0001-8338-991X

Mustafa Dağtekin 0000-0002-0797-9392

Taner Ulusinan 0009-0000-3647-0408

Project Number This work is supported by TUBITAK 1509 program number 9210017 and TUBITAK 2244 program number 119C056.
Publication Date December 29, 2023
Submission Date November 22, 2023
Acceptance Date December 1, 2023
Published in Issue Year 2023 Volume: 7 Issue: 2

Cite

APA Alsaç, A., Yenisey, M. M., Ganiz, M. C., Dağtekin, M., et al. (2023). The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem. Acta Infologica, 7(2), 360-383. https://doi.org/10.26650/acin.1394019
AMA Alsaç A, Yenisey MM, Ganiz MC, Dağtekin M, Ulusinan T. The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem. ACIN. December 2023;7(2):360-383. doi:10.26650/acin.1394019
Chicago Alsaç, Ali, Mehmet Mutlu Yenisey, Murat Can Ganiz, Mustafa Dağtekin, and Taner Ulusinan. “The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem”. Acta Infologica 7, no. 2 (December 2023): 360-83. https://doi.org/10.26650/acin.1394019.
EndNote Alsaç A, Yenisey MM, Ganiz MC, Dağtekin M, Ulusinan T (December 1, 2023) The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem. Acta Infologica 7 2 360–383.
IEEE A. Alsaç, M. M. Yenisey, M. C. Ganiz, M. Dağtekin, and T. Ulusinan, “The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem”, ACIN, vol. 7, no. 2, pp. 360–383, 2023, doi: 10.26650/acin.1394019.
ISNAD Alsaç, Ali et al. “The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem”. Acta Infologica 7/2 (December 2023), 360-383. https://doi.org/10.26650/acin.1394019.
JAMA Alsaç A, Yenisey MM, Ganiz MC, Dağtekin M, Ulusinan T. The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem. ACIN. 2023;7:360–383.
MLA Alsaç, Ali et al. “The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem”. Acta Infologica, vol. 7, no. 2, 2023, pp. 360-83, doi:10.26650/acin.1394019.
Vancouver Alsaç A, Yenisey MM, Ganiz MC, Dağtekin M, Ulusinan T. The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem. ACIN. 2023;7(2):360-83.
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