Araştırma Makalesi
BibTex RIS Kaynak Göster

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

Yıl 2023, Cilt: 7 Sayı: 2, 360 - 383, 29.12.2023
https://doi.org/10.26650/acin.1394019

Öz

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.

Destekleyen Kurum

TUBİTAK

Proje Numarası

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

Teşekkür

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

Kaynakça

  • ALAN, A., & KARABATAK, M. (2020). Veri Seti - Sınıflandırma İlişkisinde Performansa Etki Eden Faktörlerin Değerlendirilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(2). https://doi.org/10.35234/fumbd.738007 google scholar
  • Anderson D, M. G. (1992). Artificial Neural Networks Technology. Kaman Sciences Corporation, 258(6). google scholar
  • Aran, O., Yildiz, O. T., & Alpaydin, E. (2009). An incremental framework based on cross-validation for estimating the architecture of a multilayer perceptron. International Journal of Pattern Recognition and Artificial Intelligence, 23(2). https://doi.org/10.1142/S0218001409007132 google scholar
  • ARSLAN, H., ÜNEŞ, F., DEMİRCİ, M., TAŞAR, B., & YILMAZ, A. (2020). Keban Baraj Gölü Seviye Değişiminin ANFIS ve Destek Vektör Makineleri ile Tahmini. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 3(2). https://doi.org/10.47495/okufbed.748018 google scholar
  • Bharambe, Prof. P., Bagul, B., Dandekar, S., & Ingle, P. (2022). Used Car Price Prediction using Different Machine Learning Algorithms. International Journal for Research in Applied Science and Engineering Technology, 10(4). https://doi.org/10.22214/yraset.2022.41300 google scholar
  • Bhattacharya, P., Neamtiu, I., & Shelton, C. R. (2012). Automated, highly-accurate, bug assignment using machine learning and tossing graphs. Journal of Systems and Software, 85(10). https://doi.org/10.1016/j.jss.2012.04.053 google scholar
  • ÇELİK, E., DAL, D., & AYDİN, T. (2021). Duygu Analizi İçin Veri Madenciliği Sınıflandırma Algoritmalarının Karşılaştırılması. European Journal of Science and Technology. https://doi.org/10.31590/ejosat.905259 google scholar
  • Cook, D., Dixon, P., Duckworth, W. M., Kaiser, M. S., Koehler, K., Meeker, W. Q., & Stephenson, W. R. (2001). Binary Response and Logistic Regression Analysis. Project Beyond Traditional Statistical Methods, Ml. google scholar
  • Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3). https://doi.org/10.1023/A:1022627411411 google scholar
  • Dangeti, P. (2017). Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R. In Packt Publishing. google scholar
  • Deloitte, & TUBISAD. (2022). Bilgi ve İletişim Teknolojileri Sektörü 2021 Pazar Verileri. google scholar
  • Doğan, C. (2021). İstatistiksel ve Makine Öğrenme ile Derin Sinir Ağlarında Hiper-Parametre Seçimi İçin Melez Yaklaşım [Yüksek Lisans]. Hacettepe Üniversitesi. google scholar
  • Domingos, P. (2000). A Unified Bias-Variance Decomposition. Aaai/Iaai. google scholar
  • Emmert-Streib, F., & Dehmer, M. (2019). High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection. In Machine Learning and Knowledge Extraction (Vol. 1, Issue 1). https://doi.org/10.3390/make1010021 google scholar
  • Friedrich, S., Groll, A., Ickstadt, K., Kneib, T., Pauly, M., Rahnenführer, J., & Friede, T. (2023). Regularization approaches in clin-ical biostatistics: A review of methods and their applications. In Statistical Methods in Medical Research (Vol. 32, Issue 2). https://doi.org/10.1177/09622802221133557 google scholar
  • Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural Networks and the Bias/Variance Dilemma. Neural Computation, 4(1). https://doi.org/10.1162/neco.1992.4.1.1 google scholar
  • Golam Kibria, B. M., & Banik, S. (2016). Some ridge regression estimators and their performances. Journal of Modern Applied Statistical Methods, 15(1). https://doi.org/10.22237/jmasm/1462075860 google scholar
  • Goldberg, N., & Eckstein, J. (2012). Sparse weighted voting classifier selection and its linear programming relaxations. Information Processing Letters, 112(12). https://doi.org/10.1016/j.ipl.2012.03.004 google scholar
  • Ha, J., Kambe, M., & Pe, J. (2011). Data Mining: Concepts and Techniques. In Data Mining: Concepts and Techniques. https://doi.org/10.1016/C2009-0-61819-5 google scholar
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis. In Vectors. https://doi.org/10.1016/j.ypharm.2011.02.019 google scholar
  • Hautamaki, V., Kinnunen, T., Sedlak, F., Lee, K. A., Ma, B., & Li, H. (2013). Sparse classifier fusion for speaker verification. IEEE Transactions on Audio, Speech and Language Processing, 21(8). https://doi.org/10.1109/TASL.2013.2256895 google scholar
  • Helming, J., Arndt, H., Hodaie, Z., Koegel, M., & Narayan, N. (2011). Automatic Assignment of Work Items. Communications in Computer and Information Science, 230. https://doi.org/10.1007/978-3-642-23391-3_17 google scholar
  • Jonsson, L., Borg, M., Broman, D., Sandahl, K., Eldh, S., & Runeson, P. (2016). Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts. Empirical Software Engineering, 21(4). https://doi.org/10.1007/s10664-015-9401-9 google scholar
  • Koçoğlu, F. Ö., & Esnaf, Ş. (2022). Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification. International Journal of Business Analytics, 9(5). https://doi.org/10.4018/yban.298014 google scholar
  • 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
  • Mao, S., Xiong, L., Jiao, L. C., Zhang, S., & Chen, B. (2013). Weighted ensemble based on 0-1 matrix decomposition. Electronics Letters, 49(2). https://doi.org/10.1049/el.2012.3528 google scholar
  • Muller, A. C., & Guido, S. (2017). Introduction to Machine Learning with Python: a guide for data scientist. In O’Reilly Media, Inc. google scholar
  • Orynbassar, A., Sapazhanov, Y., Kadyrov, S., & Lyublinskaya, I. (2022). Application of ROC Curve Analysis for Predicting Students’ Passing Grade in a Course Based on Prerequisite Grades. Mathematics, 10(12). https://doi.org/10.3390/math10122084 google scholar
  • ÖZBİLGİN, F., & KURNAZ, Ç. (2023). Koroner Arter Hastalığının İris Görüntülerinden Yerel İkili Örüntüler ve Yapay Sinir Ağı Kullanılarak Tahmini. Karadeniz Fen Bilimleri Dergisi, 13(2). https://doi.org/10.31466/kfbd.1266996 google scholar
  • Özgür, A., Nar, F., & Erdem, H. (2018). Sparsity-driven weighted ensemble classifier. International Journal of Computational Intelligence Systems, 11 (1). https://doi.Org/10.2991/ijcis.11.1.73 google scholar
  • Paper, D. (2019). Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python. In Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python. https://doi.org/10.1007/978-1-4842-5373-1 google scholar
  • Sahoo, K., Samal, A. K., Pramanik, J., & Pani, S. K. (2019). Exploratory data analysis using python. International Journal of Innovative Technology and Exploring Engineering, 8(12), 4727-4735. https://doi.org/10.35940/jitee.L3591.1081219 google scholar
  • Şen, M. U., & Erdogan, H. (2013). Linear classifier combination and selection using group sparse regularization and hinge loss. Pattern Recognition Letters, 34(3). https://doi.org/10.1016/j.patrec.2012.10.008 google scholar
  • ŞENEL, S., & ALATLI, B. (2014). Lojistik Regresyon Analizinin Kullanıldığı Makaleler Üzerine Bir İnceleme. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 5(1). https://doi.org/10.21031/epod.67169 google scholar
  • Sinha, K., Uddin, Z., Kawsar, H. I., Islam, S., Deen, M. J., & Howlader, M.M.R. (2023). Analyzing chronic disease biomarkers using electrochem-ical sensors and artificial neural networks. In TrAC - Trends in Analytical Chemistry (Vol. 158). https://doi.org/10.1016/j.trac.2022.116861 google scholar
  • Şipal, B., Ormancı, B. B., & Altınel, A. B. (2022). KELİME ANLAM BULANIKLIĞINI GİDERMEK İÇİN DİFÜZYON REGÜLARİZASYON VE NORMALİZASYON TEKNİKLERİNİN KULLANILMASI. In MÜHENDİSLİK ALANINDA ULUSLARARASI ARAŞTIRMALAR VI (pp. 75-85). google scholar
  • Tanyildizi, E., & Demirtas, F. (2019). Hiper Parametre Optimizasyonu Hyper Parameter Optimization. 1st International Infor-matics and Software Engineering Conference: Innovative Technologies for Digital Transformation, IISEC 2019 - Proceedings. https://doi.org/10.1109/UBMYK48245.2019.8965609 google scholar
  • TAZEGÜL, A., YAZARKAN, H., & YERDELEN, C. (2016). İşletmelerin Finansal Başarılı ve Başarısız Olma Durumlarının Veri Madenciliği ve Lojistik Regresyon Analizi İle Tahmin Edilebilirliği. Ege Akademik Bakis (Ege Academic Review), 16(1). https://doi.org/10.21121/eab.2016119960 google scholar
  • Tian, Y., & Zhang, Y. (2022). A comprehensive survey on regularization strategies in machine learning. In Information Fusion (Vol. 80). https://doi.org/10.1016/j.inffus.2021.11.005 google scholar
  • Tinoco, S. L. J. L., Santos, H. G., Menotti, D., Santos, A. B., & Dos Santos, J. A. (2013). Ensemble of classifiers for remote sensed hyperspectral land cover analysis: An approach based on Linear Programming and Weighted Linear Combination. International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1109/IGARSS.2013.6723730 google scholar
  • Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. In Data Mining: Practical Machine Learning Tools and Techniques. google scholar
  • YENİSU, E. (2021). Ekonomiyi Harekete Geçiren Kilit Sektörler Nelerdir? Türkiye Üzerine Bir Girdi-Çıktı Analizi. İzmir İktisat Dergisi, 36(4). https://doi.org/10.24988/je.721302 google scholar
  • YETGINLER, B., & ATACAK, İ. (2020). Sentiment Analyses on Movie Reviews using Machine Learning-Based Methods. Artificial Intelligence Studies, 3(2). https://doi.org/10.30855/ais.2020.03.02.01 google scholar
  • Yildiz, M., Alsac, A., Ulusinan, T., Ganiz, M. C., & Yenisey, M. M. (2022). IT Support Ticket Completion Time Prediction. Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022. https://doi.org/10.1109/UBMK55850.2022.9919591 google scholar
  • Yin, X. C., Huang, K., Hao, H. W., Iqbal, K., & Wang, Z. Bin. (2012). Classifier ensemble using a heuristic learning with sparsity and diversity. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7664 LNCS(PART 2). https://doi.org/10.1007/978-3-642-34481-7_13 google scholar
  • Yoon, B.L. (1989). Artificial neural network technology. ACM SIGSMALL/PC Notes, 15(3), 3-16. https://doi.org/10.1145/74657.74658 google scholar
  • Zhang, L., & Zhou, W. Da. (2011). Sparse ensembles using weighted combination methods based on linear programming. Pattern Recognition, 44(1). https://doi.org/10.1016/j.patcog.2010.07.021 google scholar
  • Zibran, M.F. (2016). On the effectiveness of labeled latent dirichlet allocation in automatic bug-report categorization. Proceedings - International Conference on Software Engineering. https://doi.org/10.1145/2889160.2892646 google scholar

Sorun Türü Tahmini Probleminde Düzenlileştirme Yönteminin Model Başarısı Üzerindeki Etkisi

Yıl 2023, Cilt: 7 Sayı: 2, 360 - 383, 29.12.2023
https://doi.org/10.26650/acin.1394019

Öz

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.

Proje Numarası

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

Kaynakça

  • ALAN, A., & KARABATAK, M. (2020). Veri Seti - Sınıflandırma İlişkisinde Performansa Etki Eden Faktörlerin Değerlendirilmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(2). https://doi.org/10.35234/fumbd.738007 google scholar
  • Anderson D, M. G. (1992). Artificial Neural Networks Technology. Kaman Sciences Corporation, 258(6). google scholar
  • Aran, O., Yildiz, O. T., & Alpaydin, E. (2009). An incremental framework based on cross-validation for estimating the architecture of a multilayer perceptron. International Journal of Pattern Recognition and Artificial Intelligence, 23(2). https://doi.org/10.1142/S0218001409007132 google scholar
  • ARSLAN, H., ÜNEŞ, F., DEMİRCİ, M., TAŞAR, B., & YILMAZ, A. (2020). Keban Baraj Gölü Seviye Değişiminin ANFIS ve Destek Vektör Makineleri ile Tahmini. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 3(2). https://doi.org/10.47495/okufbed.748018 google scholar
  • Bharambe, Prof. P., Bagul, B., Dandekar, S., & Ingle, P. (2022). Used Car Price Prediction using Different Machine Learning Algorithms. International Journal for Research in Applied Science and Engineering Technology, 10(4). https://doi.org/10.22214/yraset.2022.41300 google scholar
  • Bhattacharya, P., Neamtiu, I., & Shelton, C. R. (2012). Automated, highly-accurate, bug assignment using machine learning and tossing graphs. Journal of Systems and Software, 85(10). https://doi.org/10.1016/j.jss.2012.04.053 google scholar
  • ÇELİK, E., DAL, D., & AYDİN, T. (2021). Duygu Analizi İçin Veri Madenciliği Sınıflandırma Algoritmalarının Karşılaştırılması. European Journal of Science and Technology. https://doi.org/10.31590/ejosat.905259 google scholar
  • Cook, D., Dixon, P., Duckworth, W. M., Kaiser, M. S., Koehler, K., Meeker, W. Q., & Stephenson, W. R. (2001). Binary Response and Logistic Regression Analysis. Project Beyond Traditional Statistical Methods, Ml. google scholar
  • Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3). https://doi.org/10.1023/A:1022627411411 google scholar
  • Dangeti, P. (2017). Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R. In Packt Publishing. google scholar
  • Deloitte, & TUBISAD. (2022). Bilgi ve İletişim Teknolojileri Sektörü 2021 Pazar Verileri. google scholar
  • Doğan, C. (2021). İstatistiksel ve Makine Öğrenme ile Derin Sinir Ağlarında Hiper-Parametre Seçimi İçin Melez Yaklaşım [Yüksek Lisans]. Hacettepe Üniversitesi. google scholar
  • Domingos, P. (2000). A Unified Bias-Variance Decomposition. Aaai/Iaai. google scholar
  • Emmert-Streib, F., & Dehmer, M. (2019). High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection. In Machine Learning and Knowledge Extraction (Vol. 1, Issue 1). https://doi.org/10.3390/make1010021 google scholar
  • Friedrich, S., Groll, A., Ickstadt, K., Kneib, T., Pauly, M., Rahnenführer, J., & Friede, T. (2023). Regularization approaches in clin-ical biostatistics: A review of methods and their applications. In Statistical Methods in Medical Research (Vol. 32, Issue 2). https://doi.org/10.1177/09622802221133557 google scholar
  • Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural Networks and the Bias/Variance Dilemma. Neural Computation, 4(1). https://doi.org/10.1162/neco.1992.4.1.1 google scholar
  • Golam Kibria, B. M., & Banik, S. (2016). Some ridge regression estimators and their performances. Journal of Modern Applied Statistical Methods, 15(1). https://doi.org/10.22237/jmasm/1462075860 google scholar
  • Goldberg, N., & Eckstein, J. (2012). Sparse weighted voting classifier selection and its linear programming relaxations. Information Processing Letters, 112(12). https://doi.org/10.1016/j.ipl.2012.03.004 google scholar
  • Ha, J., Kambe, M., & Pe, J. (2011). Data Mining: Concepts and Techniques. In Data Mining: Concepts and Techniques. https://doi.org/10.1016/C2009-0-61819-5 google scholar
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis. In Vectors. https://doi.org/10.1016/j.ypharm.2011.02.019 google scholar
  • Hautamaki, V., Kinnunen, T., Sedlak, F., Lee, K. A., Ma, B., & Li, H. (2013). Sparse classifier fusion for speaker verification. IEEE Transactions on Audio, Speech and Language Processing, 21(8). https://doi.org/10.1109/TASL.2013.2256895 google scholar
  • Helming, J., Arndt, H., Hodaie, Z., Koegel, M., & Narayan, N. (2011). Automatic Assignment of Work Items. Communications in Computer and Information Science, 230. https://doi.org/10.1007/978-3-642-23391-3_17 google scholar
  • Jonsson, L., Borg, M., Broman, D., Sandahl, K., Eldh, S., & Runeson, P. (2016). Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts. Empirical Software Engineering, 21(4). https://doi.org/10.1007/s10664-015-9401-9 google scholar
  • Koçoğlu, F. Ö., & Esnaf, Ş. (2022). Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification. International Journal of Business Analytics, 9(5). https://doi.org/10.4018/yban.298014 google scholar
  • 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
  • Mao, S., Xiong, L., Jiao, L. C., Zhang, S., & Chen, B. (2013). Weighted ensemble based on 0-1 matrix decomposition. Electronics Letters, 49(2). https://doi.org/10.1049/el.2012.3528 google scholar
  • Muller, A. C., & Guido, S. (2017). Introduction to Machine Learning with Python: a guide for data scientist. In O’Reilly Media, Inc. google scholar
  • Orynbassar, A., Sapazhanov, Y., Kadyrov, S., & Lyublinskaya, I. (2022). Application of ROC Curve Analysis for Predicting Students’ Passing Grade in a Course Based on Prerequisite Grades. Mathematics, 10(12). https://doi.org/10.3390/math10122084 google scholar
  • ÖZBİLGİN, F., & KURNAZ, Ç. (2023). Koroner Arter Hastalığının İris Görüntülerinden Yerel İkili Örüntüler ve Yapay Sinir Ağı Kullanılarak Tahmini. Karadeniz Fen Bilimleri Dergisi, 13(2). https://doi.org/10.31466/kfbd.1266996 google scholar
  • Özgür, A., Nar, F., & Erdem, H. (2018). Sparsity-driven weighted ensemble classifier. International Journal of Computational Intelligence Systems, 11 (1). https://doi.Org/10.2991/ijcis.11.1.73 google scholar
  • Paper, D. (2019). Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python. In Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python. https://doi.org/10.1007/978-1-4842-5373-1 google scholar
  • Sahoo, K., Samal, A. K., Pramanik, J., & Pani, S. K. (2019). Exploratory data analysis using python. International Journal of Innovative Technology and Exploring Engineering, 8(12), 4727-4735. https://doi.org/10.35940/jitee.L3591.1081219 google scholar
  • Şen, M. U., & Erdogan, H. (2013). Linear classifier combination and selection using group sparse regularization and hinge loss. Pattern Recognition Letters, 34(3). https://doi.org/10.1016/j.patrec.2012.10.008 google scholar
  • ŞENEL, S., & ALATLI, B. (2014). Lojistik Regresyon Analizinin Kullanıldığı Makaleler Üzerine Bir İnceleme. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, 5(1). https://doi.org/10.21031/epod.67169 google scholar
  • Sinha, K., Uddin, Z., Kawsar, H. I., Islam, S., Deen, M. J., & Howlader, M.M.R. (2023). Analyzing chronic disease biomarkers using electrochem-ical sensors and artificial neural networks. In TrAC - Trends in Analytical Chemistry (Vol. 158). https://doi.org/10.1016/j.trac.2022.116861 google scholar
  • Şipal, B., Ormancı, B. B., & Altınel, A. B. (2022). KELİME ANLAM BULANIKLIĞINI GİDERMEK İÇİN DİFÜZYON REGÜLARİZASYON VE NORMALİZASYON TEKNİKLERİNİN KULLANILMASI. In MÜHENDİSLİK ALANINDA ULUSLARARASI ARAŞTIRMALAR VI (pp. 75-85). google scholar
  • Tanyildizi, E., & Demirtas, F. (2019). Hiper Parametre Optimizasyonu Hyper Parameter Optimization. 1st International Infor-matics and Software Engineering Conference: Innovative Technologies for Digital Transformation, IISEC 2019 - Proceedings. https://doi.org/10.1109/UBMYK48245.2019.8965609 google scholar
  • TAZEGÜL, A., YAZARKAN, H., & YERDELEN, C. (2016). İşletmelerin Finansal Başarılı ve Başarısız Olma Durumlarının Veri Madenciliği ve Lojistik Regresyon Analizi İle Tahmin Edilebilirliği. Ege Akademik Bakis (Ege Academic Review), 16(1). https://doi.org/10.21121/eab.2016119960 google scholar
  • Tian, Y., & Zhang, Y. (2022). A comprehensive survey on regularization strategies in machine learning. In Information Fusion (Vol. 80). https://doi.org/10.1016/j.inffus.2021.11.005 google scholar
  • Tinoco, S. L. J. L., Santos, H. G., Menotti, D., Santos, A. B., & Dos Santos, J. A. (2013). Ensemble of classifiers for remote sensed hyperspectral land cover analysis: An approach based on Linear Programming and Weighted Linear Combination. International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1109/IGARSS.2013.6723730 google scholar
  • Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. In Data Mining: Practical Machine Learning Tools and Techniques. google scholar
  • YENİSU, E. (2021). Ekonomiyi Harekete Geçiren Kilit Sektörler Nelerdir? Türkiye Üzerine Bir Girdi-Çıktı Analizi. İzmir İktisat Dergisi, 36(4). https://doi.org/10.24988/je.721302 google scholar
  • YETGINLER, B., & ATACAK, İ. (2020). Sentiment Analyses on Movie Reviews using Machine Learning-Based Methods. Artificial Intelligence Studies, 3(2). https://doi.org/10.30855/ais.2020.03.02.01 google scholar
  • Yildiz, M., Alsac, A., Ulusinan, T., Ganiz, M. C., & Yenisey, M. M. (2022). IT Support Ticket Completion Time Prediction. Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022. https://doi.org/10.1109/UBMK55850.2022.9919591 google scholar
  • Yin, X. C., Huang, K., Hao, H. W., Iqbal, K., & Wang, Z. Bin. (2012). Classifier ensemble using a heuristic learning with sparsity and diversity. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7664 LNCS(PART 2). https://doi.org/10.1007/978-3-642-34481-7_13 google scholar
  • Yoon, B.L. (1989). Artificial neural network technology. ACM SIGSMALL/PC Notes, 15(3), 3-16. https://doi.org/10.1145/74657.74658 google scholar
  • Zhang, L., & Zhou, W. Da. (2011). Sparse ensembles using weighted combination methods based on linear programming. Pattern Recognition, 44(1). https://doi.org/10.1016/j.patcog.2010.07.021 google scholar
  • Zibran, M.F. (2016). On the effectiveness of labeled latent dirichlet allocation in automatic bug-report categorization. Proceedings - International Conference on Software Engineering. https://doi.org/10.1145/2889160.2892646 google scholar
Toplam 51 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Memnuniyet ve Optimizasyon, Modelleme ve Simülasyon
Bölüm Araştırma Makalesi
Yazarlar

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

Proje Numarası This work is supported by TUBITAK 1509 program number 9210017 and TUBITAK 2244 program number 119C056.
Yayımlanma Tarihi 29 Aralık 2023
Gönderilme Tarihi 22 Kasım 2023
Kabul Tarihi 1 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 7 Sayı: 2

Kaynak Göster

APA Alsaç, A., Yenisey, M. M., Ganiz, M. C., Dağtekin, M., vd. (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. Aralık 2023;7(2):360-383. doi:10.26650/acin.1394019
Chicago Alsaç, Ali, Mehmet Mutlu Yenisey, Murat Can Ganiz, Mustafa Dağtekin, ve Taner Ulusinan. “The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem”. Acta Infologica 7, sy. 2 (Aralık 2023): 360-83. https://doi.org/10.26650/acin.1394019.
EndNote Alsaç A, Yenisey MM, Ganiz MC, Dağtekin M, Ulusinan T (01 Aralık 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, ve T. Ulusinan, “The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem”, ACIN, c. 7, sy. 2, ss. 360–383, 2023, doi: 10.26650/acin.1394019.
ISNAD Alsaç, Ali vd. “The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem”. Acta Infologica 7/2 (Aralık 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 vd. “The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem”. Acta Infologica, c. 7, sy. 2, 2023, ss. 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.