Effect of Hyperparameter Tuning Using Random Search on Tree-Based Classification Algorithm for Software Defect Prediction
Muhammad Hevny Rizky(1), Mohammad Reza Faisal(2*), Irwan Budiman(3), Dwi Kartini(4), Friska Abadi(5)
(1) Lambung Mangkurat University
(2) Lambung Mangkurat University
(3) Lambung Mangkurat University
(4) Lambung Mangkurat University
(5) Lambung Mangkurat University
(*) Corresponding Author
Abstract
Keywords
Full Text:
PDFReferences
A. Elmishali and M. Kalech, “Issues-Driven features for software fault prediction,” Information and Software Technology, vol. 155, 2023, doi: 10.1016/j.infsof.2022.107102. [2] M. K. Thota, F. H. Shajin, and P. Rajesh, “Survey on software defect prediction techniques,” International Journal of Applied Science and Engineering, vol. 17, no. 4, pp. 331–344, 2020, doi: 10.6703/IJASE.202012_17(4).331. [3] W. Zheng, S. Mo, X. Jin, Y. Qu, Z. Xie, and J. Shuai, “Software defect prediction model based on improved deep forest and AutoEncoder by forest,” Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE, vol. 2019-July, no. 3, pp. 419–424, 2019, doi: 10.18293/SEKE2019-008. [4] M. A. Mabayoje, A. O. Balogun, H. A. Jibril, J. O. Atoyebi, H. A. Mojeed, and V. E. Adeyemo, “Parameter tuning in KNN for software defect prediction: an empirical analysis,” Jurnal Teknologi dan Sistem Komputer, vol. 7, no. 4, pp. 121–126, 2019, doi: 10.14710/jtsiskom.7.4.2019.121-126. [5] E. Andini, M. Reza Faisal, R. Herteno, R. Adi Nugroho, and F. Abadi, “PENINGKATAN KINERJA PREDIKSI CACAT SOFTWARE DENGAN HYPERPARAMETER TUNING PADA ALGORITMA KLASIFIKASI DEEP FOREST,” Jurnal MNEMONIC, vol. 5, no. 2, 2022, [Online]. Available: https://github.com/bharlow058/AEEEM-and-other- [6] M. Ryan Afrizal, R. Adi Nugroho, D. Kartini, R. Herteno, J. Ahmad Yani Km, and K. Selatan, “XGBOOST DENGAN RANDOM SEARCH HYPER-PARAMETER TUNING UNTUK KLASIFIKASI SITUS PHISING,” 2022. [7] T. Zhou, X. Sun, X. Xia, B. Li, and X. Chen, “Improving defect prediction with deep forest,” Information and Software Technology, vol. 114, no. July 2018, pp. 204–216, 2019, doi: 10.1016/j.infsof.2019.07.003. [8] A. Javeed, S. Zhou, L. Yongjian, I. Qasim, A. Noor, and R. Nour, “An Intelligent Learning System Based on Random Search Algorithm and Optimized Random Forest Model for Improved Heart Disease Detection,” IEEE Access, vol. 7, pp. 180235–180243, 2019, doi: 10.1109/ACCESS.2019.2952107. [9] H. Aji Prihanditya and N. Hestu Aji Prihanditya, “The Implementation of Z-Score Normalization and Boosting Techniques to Increase Accuracy of C4.5 Algorithm in Diagnosing Chronic Kidney Disease,” 2020. [10] B. Kovalerchuk, “Enhancement of Cross Validation Using Hybrid Visual and Analytical Means with Shannon Function,” Studies in Computational Intelligence, vol. 835, pp. 517–543, 2020, doi: 10.1007/978-3-030-31041-7_29. [11] H. Aljamaan and A. Alazba, “Software defect prediction using tree-based ensembles,” PROMISE 2020 - Proceedings of the 16th ACM International Conference on Predictive Models and Data Analytics in Software Engineering, Co-located with ESEC/FSE 2020, pp. 1–10, 2020, doi: 10.1145/3416508.3417114. [12] M. Joye and F. Salehi, Private yet efficient decision tree evaluation, vol. 10980 LNCS. Springer International Publishing, 2018. doi: 10.1007/978-3-319-95729-6_16. [13] B. Charbuty and A. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 20–28, 2021, doi: 10.38094/jastt20165. [14] H. B. Kibria and A. Matin, “THE S EVERITY P REDICTION OF T HE B INARY A ND M ULTI -C LASS C ARDIOVASCULAR D ISEASE - A M ACHINE L EARNING -B ASED F USION A PPROACH,” 2022. [15] L. V. Utkin, “An imprecise deep forest for classification,” Expert Systems with Applications, vol. 141, p. 112978, 2020, doi: 10.1016/j.eswa.2019.112978. [16] S. Cui, Y. Yin, D. Wang, Z. Li, and Y. Wang, “A stacking-based ensemble learning method for earthquake casualty prediction,” Applied Soft Computing, vol. 101, p. 107038, 2021, doi: 10.1016/j.asoc.2020.107038. [17] H. Alibrahim and L. Simone A., “2021 IEEE Congress on Evolutionary Computation,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 4, no. 5, pp. 740–740, 2020, doi: 10.1109/tetci.2020.3020707. [18] R. G. Mantovani, T. Horváth, R. Cerri, S. B. Junior, J. Vanschoren, and A. C. P. de L. F. de Carvalho, “An empirical study on hyperparameter tuning of decision trees,” no. December, 2018, [Online]. Available: http://arxiv.org/abs/1812.02207 [19] M. Daviran, A. Maghsoudi, R. Ghezelbash, and B. Pradhan, “Computers and Geosciences A new strategy for spatial predictive mapping of mineral prospectivity : Automated hyperparameter tuning of random forest approach,” Computers and Geosciences, vol. 148, no. January, p. 104688, 2021, doi: 10.1016/j.cageo.2021.104688.
DOI: https://doi.org/10.22146/ijccs.90437
Article Metrics
Abstract views : 1774 | views : 1076Refbacks
- There are currently no refbacks.
Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1