TY  - JOUR
T1  - The Efficiency of Regularization Method on Model Success in Issue Type Prediction Problem
TT  - Sorun Türü Tahmini Probleminde Düzenlileştirme Yönteminin Model Başarısı Üzerindeki Etkisi
AU  - Alsaç, Ali
AU  - Yenisey, Mehmet Mutlu
AU  - Ganiz, Murat Can
AU  - Dağtekin, Mustafa
AU  - Ulusinan, Taner
PY  - 2023
DA  - December
Y2  - 2023
DO  - 10.26650/acin.1394019
JF  - Acta Infologica
JO  - ACIN
PB  - Istanbul University
WT  - DergiPark
SN  - 2602-3563
SP  - 360
EP  - 383
VL  - 7
IS  - 2
LA  - en
AB  - 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.
KW  - IT service management
KW  - regularization
KW  - prediction
KW  - classification
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UR  - https://doi.org/10.26650/acin.1394019
L1  - https://dergipark.org.tr/en/download/article-file/3551155
ER  -