Analysis of Gradient Boosting, XGBoost, and CatBoost on Mobile Phone Classification
DOI:
10.47709/cnahpc.v6i2.3790Keywords:
Mobile phone classification, Gradient Boosting, XGBoost, CatBoost, NormalizationDimension Badge Record
Abstract
In the ever-evolving landscape of mobile phone technology, accurately classifying device specifications is paramount for market analysis and consumer decision-making. This research conducts a comprehensive analysis of mobile phone specification classification using three prominent machine learning algorithms: Gradient Boosting, XGBoost, and CatBoost. Through meticulous dataset acquisition and preprocessing steps, including resolution normalization and price categorization, features essential for classification analysis were standardized. Robust cross-validation techniques were employed to assess model performance effectively. The study demonstrates the significant impact of normalization techniques on improving model performance across all algorithms and fold variations. CatBoost consistently emerges as the top-performing algorithm, followed closely by XGBoost, with Gradient Boosting displaying respectable performance. Notably, CatBoost consistently achieves the highest AUC values and accuracy scores, demonstrating superior performance in accurately classifying mobile phone specifications. These findings underscore the importance of preprocessing methods and algorithm selection in achieving optimal classification results. For mobile phone manufacturers, leveraging machine learning algorithms for effective classification can inform product development strategies, optimizing offerings based on consumer preferences. Similarly, for data analysts, employing appropriate preprocessing techniques and algorithmic approaches can lead to more accurate predictions and informed decision-making. Future research avenues include exploring advanced preprocessing methods, investigating alternative algorithms, and incorporating additional features or datasets to enrich the classification process. Overall, this research contributes to understanding mobile phone specification classification through machine learning methodologies, offering actionable insights for industry practitioners and researchers to address evolving market dynamics and consumer preferences.
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References
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Siringoringo, R., Perangin Angin, R., & Rumahorbo, B. (2022). Model Klasifikasi Genetic-XGBoost Dengan T-Distributed Stochastic Neighbor Embedding Pada Peramalan Pasar. Jurnal Times, XI(1), 30–36. Retrieved from https://archive.ics.uci.edu/ml/datasets/online+retail
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Adler, A. I., & Painsky, A. (2022). Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection. Entropy, 24(5). https://doi.org/10.3390/e24050687
Ampomah, E. K., Qin, Z., & Nyame, G. (2020). Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement. Information, 11(6), 332. https://doi.org/10.3390/info11060332
Aravind, K. R. N. V. V. D., Shyry, S. P., & Felix, Y. (2019). Classification of Healthy and Rot Leaves of Apple Using Gradient Boosting and Support Vector Classifier. International Journal of Innovative Technology and Exploring Engineering, 8(12), 2868–2872. https://doi.org/10.35940/ijitee.L3049.1081219
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Boldini, D., Grisoni, F., Kuhn, D., Friedrich, L., & Sieber, S. A. (2023). Practical guidelines for the use of gradient boosting for molecular property prediction. Journal of Cheminformatics, 15(1), 1–13. https://doi.org/10.1186/s13321-023-00743-7
Breskuvien, D., & Dzemyda, G. (2023). Categorical Feature Encoding Techniques for Improved Classifier Performance when Dealing with Imbalanced Data of Fraudulent Transactions. International Journal of Computers, Communications and Control, 18(3), 1–17. https://doi.org/10.15837/ijccc.2023.3.5433
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Chang, W., Wang, X., Yang, J., & Qin, T. (2023). An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries. Sensors, 23(4). https://doi.org/10.3390/s23041811
Chen, T., Samaranayake, P., Cen, X. Y., Qi, M., & Lan, Y. C. (2022). The Impact of Online Reviews on Consumers’ Purchasing Decisions: Evidence From an Eye-Tracking Study. Frontiers in Psychology, 13(June). https://doi.org/10.3389/fpsyg.2022.865702
Devos, L., Meert, W., & Davis, J. (2020). Fast Gradient Boosting Decision Trees with Bit-Level Data Structures. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11906 LNAI, 590–606. https://doi.org/10.1007/978-3-030-46150-8_35
Dutta, A. K., & Wahab Sait, A. R. (2024). A Fine-Tuned CatBoost-Based Speech Disorder Detection Model. Journal of Disability Research, 3(3), 1–8. https://doi.org/10.57197/JDR-2024-0027
Dwinanda, M. W., Satyahadewi, N., & Andani, W. (2023). Classification of Student Graduation Status Using Xgboost Algorithm. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 17(3), 1785–1794. https://doi.org/10.30598/barekengvol17iss3pp1785-1794
Fayaz, M., Khan, A., Rahman, J. U., Alharbi, A., Uddin, M. I., & Alouffi, B. (2020). Ensemble machine learning model for classification of spam product reviews. Complexity, 2020. https://doi.org/10.1155/2020/8857570
Fedorov, N., & Petrichenko, Y. (2020). Gradient Boosting–Based Machine Learning Methods in Real Estate Market Forecasting. Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020), 174(Itids), 203–208. Paris, France: Atlantis Press. https://doi.org/10.2991/aisr.k.201029.039
Gonçalves Freitas, L. J., Edokawa, P. S. D., Carvalho Valadares Rodrigues, T., Thomé de Farias, A. H., & Rodrigues de Alencar, E. (2023). Catboost algorithm application in legal texts and UN 2030 Agenda. Revista de Informatica Teorica e Aplicada, 30(2), 51–58. https://doi.org/10.22456/2175-2745.128836
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Hussain, S., Mustafa, M. W., Jumani, T. A., Baloch, S. K., Alotaibi, H., Khan, I., & Khan, A. (2021). A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection. Energy Reports, 7, 4425–4436. https://doi.org/10.1016/j.egyr.2021.07.008
Ibrahim, A. A., Ridwan, R. L., Muhammed, M. M., Abdulaziz, R. O., & Saheed, G. A. (2020). Comparison of the CatBoost Classifier with other Machine Learning Methods. International Journal of Advanced Computer Science and Applications, 11(11), 738–748. https://doi.org/10.14569/IJACSA.2020.0111190
Jabeur, S. Ben, Gharib, C., Mefteh-Wali, S., & Arfi, W. Ben. (2021). CatBoost model and artificial intelligence techniques for corporate failure prediction. Technological Forecasting and Social Change, 166(February), 120658. https://doi.org/10.1016/j.techfore.2021.120658
Jabeur, S. Ben, Mefteh-Wali, S., & Viviani, J. L. (2024). Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Annals of Operations Research, 334(1–3), 679–699. https://doi.org/10.1007/s10479-021-04187-w
Jasman, T. Z., Fadhlullah, M. A., Pratama, A. L., & Rismayani, R. (2022). Analisis Algoritma Gradient Boosting, Adaboost dan Catboost dalam Klasifikasi Kualitas Air. Jurnal Teknik Informatika Dan Sistem Informasi, 8(2), 392–402. https://doi.org/10.28932/jutisi.v8i2.4906
Jinan, A., Situmorang, Z., & Rosnelly, R. (2023). Bulldog Breed Classification Using VGG-19 and Ensemble Learning. International Conference on Information Science and Technology Innovation (ICoSTEC), 2(1), 29–33. https://doi.org/10.35842/icostec.v2i1.29
Kabeyi, M. J. B. (2018). Michael porter’s five competitive forces and generetic strategies, market segmentation strategy and case study of competition in global smartphone manufacturing industry. International Journal of Applied Research, 4(10), 39–45. https://doi.org/10.22271/allresearch.2018.v4.i10a.5275
Karanikola, A., Davrazos, G., Liapis, C. M., & Kotsiantis, S. (2023). Financial sentiment analysis: Classic methods vs. deep learning models. Intelligent Decision Technologies, 17(4), 893–915. https://doi.org/10.3233/IDT-230478
Kumar, V., Kedam, N., Sharma, K. V., Mehta, D. J., & Caloiero, T. (2023). Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models. Water (Switzerland), 15(14). https://doi.org/10.3390/w15142572
Liew, X. Y., Hameed, N., & Clos, J. (2021). An investigation of XGBoost-based algorithm for breast cancer classification. Machine Learning with Applications, 6(September), 100154. https://doi.org/10.1016/j.mlwa.2021.100154
Lin, C. H., Hsu, P. I., Tseng, C. D., Chao, P. J., Wu, I. T., Ghose, S., … Lee, T. F. (2023). Application of artificial intelligence in endoscopic image analysis for the diagnosis of a gastric cancer pathogen-Helicobacter pylori infection. Scientific Reports, 13(1), 1–12. https://doi.org/10.1038/s41598-023-40179-5
Lo, Y. T., Liao, J. C. hen, Chen, M. H., Chang, C. M., & Li, C. Te. (2021). Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms. BMC Medical Informatics and Decision Making, 21(1), 1–11. https://doi.org/10.1186/s12911-021-01639-y
Neelakandan, S., & Paulraj, D. (2020). A gradient boosted decision tree-based sentiment classification of twitter data. International Journal of Wavelets, Multiresolution and Information Processing, 18(4). https://doi.org/10.1142/S0219691320500277
Pardede, D., Firmansyah, I., Handayani, M., Riandini, M., & Rosnelly, R. (2022). Comparison Of Multilayer Perceptron’s Activation And Optimization Functions In Classification Of Covid-19 Patients. JURTEKSI (Jurnal Teknologi Dan Sistem Informasi), 8(3), 271–278. https://doi.org/10.33330/jurteksi.v8i3.1482
Pardede, D., & Hayadi, B. H. (2023). Klasifikasi Sentimen Terhadap Gelaran MotoGP Mandalika 2022 Menggunakan Machine Learning. Jurnal TRANSFORMATIKA, 20(2), 42–50.
Putri, D. J., Dwifebri, M., & Adiwijaya, A. (2023). Text Classification of Indonesian Translated Hadith Using XGBoost Model and Chi-Square Feature Selection. Building of Informatics, Technology and Science (BITS), 4(4), 1732–1738. https://doi.org/10.47065/bits.v4i4.2944
Qinghe, Z., Wen, X., Boyan, H., Jong, W., & Junlong, F. (2022). Optimised extreme gradient boosting model for short term electric load demand forecasting of regional grid system. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-22024-3
Safaei, N., Safaei, B., Seyedekrami, S., Talafidaryani, M., Masoud, A., Wang, S., … Moqri, M. (2022). E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database. In PLoS ONE (Vol. 17). https://doi.org/10.1371/journal.pone.0262895
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Siringoringo, R., Perangin Angin, R., & Rumahorbo, B. (2022). Model Klasifikasi Genetic-XGBoost Dengan T-Distributed Stochastic Neighbor Embedding Pada Peramalan Pasar. Jurnal Times, XI(1), 30–36. Retrieved from https://archive.ics.uci.edu/ml/datasets/online+retail
Sopiyan, M., Fauziah, F., & Wijaya, Y. F. (2022). Fraud Detection Using Random Forest Classifier, Logistic Regression, and Gradient Boosting Classifier Algorithms on Credit Cards. JUITA: Jurnal Informatika, 10(1), 77. https://doi.org/10.30595/juita.v10i1.12050
Suhendra, R., Husdayanti, N., Suryadi, S., Juliwardi, I., Sanusi, S., Ridho, A., … Ikhsan, I. (2023). Cardiovascular Disease Prediction Using Gradient Boosting Classifier. Infolitika Journal of Data Science, 1(2), 56–62. https://doi.org/10.60084/ijds.v1i2.131
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Accepted Date: 2024-04-23
Published Date: 2024-04-28
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Copyright (c) 2024 Agus Fahmi Limas Ptr, Muhammad Mizan Siregar, Irwan Daniel
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