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Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

Anastasya Latubessy, Retantyo Wardoyo, Aina Musdholifah, Sri Kusrohmaniah

Abstract


Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used.

 

Doi: 10.28991/HIJ-2025-06-01-05

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Keywords


Classification; Attribute Weighted; Fine-Tune; Naïve Bayes; Instances Reduction; Gaming Disorder.

References


W.H.O. (2018). ICD-11 Chapter 06 Mental, behavioural or neurodevelopmental disorders. ICD-11 International Classification of Diseases 11th Revision, World Health Organization, World Health Organization, Geneva, Switzerland.

Jap, T., Tiatri, S., Jaya, E. S., & Suteja, M. S. (2013). The Development of Indonesian Online Game Addiction Questionnaire. PLoS ONE, 8(4), 4–8. doi:10.1371/journal.pone.0061098.

Kurniasanti, K. S., Wiguna, T., Wiwie, M., & Winarsih, N. S. (2018). Internet addiction among adolescents in Jakarta: A challenging situation for mental health development. Journal of International Dental and Medical Research, 11(2), 711–717.

Siste, K., Hanafi, E., Sen, L. T., Wahjoepramono, P. O. P., Kurniawan, A., & Yudistiro, R. (2021). Potential correlates of internet gaming disorder among Indonesian medical students: Cross-sectional study. Journal of Medical Internet Research, 23(4), e25468. doi:10.2196/25468.

Erik, S., & Syenshie, W. V. (2020). Relationship Between Duration of Playing Online Games and Mental Health in Male Adolescents. Scientific Journal of Mental Health, 2(2), 69–75.

Ikbal, I., Wikanengsih, W., & Septian, M. R. (2021). Profile of Online Game Addiction Level of Students in Class Xma Plus Al Mujammil Garut. FOKUS (Guidance & Counseling Study in Education), 4(1), 56. doi:10.22460/fokus.v4i1.6138.

King, D. L., & Delfabbro, P. H. (2018). Internet Gaming Disorder: Theory, Assessment, Treatment, and Prevention. In Internet Gaming Disorder: Theory, Assessment, Treatment, and Prevention, 1–276. doi:10.1016/C2016-0-04107-4.

King, D. L., Delfabbro, P. H., Wu, A. M. S., Doh, Y. Y., Kuss, D. J., Pallesen, S., Mentzoni, R., Carragher, N., & Sakuma, H. (2017). Treatment of Internet gaming disorder: An international systematic review and CONSORT evaluation. Clinical Psychology Review, 54(November 2016), 123–133. doi:10.1016/j.cpr.2017.04.002.

Mihara, S., & Higuchi, S. (2017). Cross-sectional and longitudinal epidemiological studies of Internet gaming disorder: A systematic review of the literature. Psychiatry and Clinical Neurosciences, 71(7), 425–444. doi:10.1111/pcn.12532.

Kaplan, H. I., & Sadock, B. J. (1988). Synopsis of psychiatry: Behavioral sciences clinical psychiatry. Williams & Wilkins Co, Baltimore, United States.

Yarkoni, T. (2012). Psychoinformatics: New Horizons at the Interface of the Psychological and Computing Sciences. Current Directions in Psychological Science, 21(6), 391–397. doi:10.1177/0963721412457362.

Pandey, D. (2020). Psychoinformatics: a theoretical approach on information science and psychology. Journal of Management and Science, 10(2), 7–10. doi:10.26524/jms.2020.2.2.

Baumeister, H., & Montag, C. (2019). Digital Phenotyping and Mobile Sensing. Digital Phenotyping and Mobile Sensing: New Developments in Psychoinformatics, 466. doi:10.1007/978-3-030-98546-2.

Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine Learning Approaches for Clinical Psychology and Psychiatry. Annual Review of Clinical Psychology, 14, 91–118. doi:10.1146/annurev-clinpsy-032816-045037.

Stephens, C. R., Huerta, H. F., & Linares, A. R. (2018). When is the Naive Bayes approximation not so naive? Machine Learning, 107(2), 397–441. doi:10.1007/s10994-017-5658-0.

Zhang, H., Jiang, L., & Yu, L. (2021). Attribute and instance weighted naive Bayes. Pattern Recognition, 111, 107674. doi:10.1016/j.patcog.2020.107674.

Bai, Y., & Bain, M. (2022). Optimizing weighted lazy learning and Naive Bayes classification using differential evolution algorithm. Journal of Ambient Intelligence and Humanized Computing, 13(6), 3005–3024. doi:10.1007/s12652-021-03135-7.

Zhang, H., & Jiang, L. (2022). Fine tuning attribute weighted naive Bayes. Neurocomputing, 488, 402–411. doi:10.1016/j.neucom.2022.03.020.

Jiang, L., Zhang, L., Li, C., & Wu, J. (2019). A Correlation-Based Feature Weighting Filter for Naive Bayes. IEEE Transactions on Knowledge and Data Engineering, 31(2), 201–213. doi:10.1109/TKDE.2018.2836440.

El Hindi, K. (2014). Fine tuning the Naïve Bayesian learning algorithm. AI Communications, 27(2), 133–141. doi:10.3233/AIC-130588.

Elkan, C. (1997). Boosting And Naive Bayesian Learning. Department of Computer Science and Engineering University of California, San Diego La Jolla, California, United States.

Langley, P., Iba, W., & Thompson, K. (1992). Analysis of Bayesian classifiers. Proceedings Tenth National Conference on Artificial Intelligence, 223–228.

Jamjoom, M., & El Hindi, K. (2016). Partial instance reduction for noise elimination. Pattern Recognition Letters, 74, 30–37. doi:10.1016/j.patrec.2016.01.021.

Morris, B. (2001). 54th World Health Assembly. International Journal of Health Care Quality Assurance, 14(6), 5. doi:10.1108/ijhcqa.2001.06214fab.005.

Funk, M. (2016). Global burden of mental disorders and the need for a comprehensive, coordinated response from health and social sectors at the country level. World Health Organization, World Health Organization, Geneva, Switzerland.

Rahmadani, A., Setianingsih, C., Dirgantara, F. M., Ambarita, A. R., Arifin, H. I., Manalu, I. P., & Pratama, M. L. (2023). Depression, anxiety, and stress disorders detection in students during the Covid-19 pandemic using Naïve Bayes algorithm. BIO Web of Conferences, 75, 1003. doi:10.1051/bioconf/20237501003.

Jin, Y., Xu, S., Shao, Z., Luo, X., Wang, Y., Yu, Y., & Wang, Y. (2024). Discovery of depression-associated factors among childhood trauma victims from a large sample size: Using machine learning and network analysis. Journal of Affective Disorders, 345(September 2023), 300–310. doi:10.1016/j.jad.2023.10.101.

Zhong, Y., He, J., Luo, J., Zhao, J., Cen, Y., Song, Y., Wu, Y., Lin, C., Pan, L., & Luo, J. (2024). A machine learning algorithm-based model for predicting the risk of non-suicidal self-injury among adolescents in western China: A multicentre cross-sectional study. Journal of Affective Disorders, 345(May 2023), 369–377. doi:10.1016/j.jad.2023.10.110.

Liu, Q., Zhou, B., Zhang, X., Qing, P., Zhou, X., Zhou, F., Xu, X., Zhu, S., Dai, J., Huang, Y., Wang, J., Zou, Z., Kendrick, K. M., Becker, B., & Zhao, W. (2023). Abnormal multi-layered dynamic cortico-subcortical functional connectivity in major depressive disorder and generalized anxiety disorder. Journal of Psychiatric Research, 167(May), 23–31. doi:10.1016/j.jpsychires.2023.10.004.

Saha, D. K., Hossain, T., Safran, M., Alfarhood, S., Mridha, M. F., & Che, D. (2024). Ensemble of hybrid model based technique for early detecting of depression based on SVM and neural networks. Scientific Reports, 14(1), 1–18. doi:10.1038/s41598-024-77193-0.

Ananthanagu, U., & Agarwal, P. (2024). DepXGBoot: Depression detection using a robust tuned extreme gradient boosting model generator. IAES International Journal of Artificial Intelligence, 13(4), 4352–4363. doi:10.11591/ijai.v13.i4.pp4352-4363.

Rahman, A. A., Khalid, L. I., Siraji, M. I., Nishat, M. M., Faisal, F., & Ahmed, A. (2021). Enhancing the Performance of Machine Learning Classifiers by Hyperparameter Optimization in Detecting Anxiety Levels of Online Gamers. 24th International Conference on Computer and Information Technology, ICCIT 2021, 18–20. doi:10.1109/ICCIT54785.2021.9689911.

Ioannidis, K., Chamberlain, S. R., Treder, M. S., Kiraly, F., Leppink, E. W., Redden, S. A., Stein, D. J., Lochner, C., & Grant, J. E. (2016). Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry. Journal of Psychiatric Research, 83, 94–102. doi:10.1016/j.jpsychires.2016.08.010.

Nandhini, C., & Krishnaveni, K. (2016). Evaluation of internet addiction disorder among students. Indian Journal of Science and Technology, 9(19), 93864. doi:10.17485/ijst/2016/v9i19/93864.

van Eeden, W. A., Luo, C., van Hemert, A. M., Carlier, I. V. E., Penninx, B. W., Wardenaar, K. J., Hoos, H., & Giltay, E. J. (2021). Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression. Psychiatry Research, 299. doi:10.1016/j.psychres.2021.113823.

Jain, P., Srinivas, K. R., & Vichare, A. (2022). Depression and Suicide Analysis Using Machine Learning and NLP. Journal of Physics: Conference Series, 2161(1). doi:10.1088/1742-6596/2161/1/012034.

Haque, U. M., Kabir, E., & Khanam, R. (2021). Detection of child depression using machine learning methods. PLoS ONE, 16(12 December 2021), 1–13. doi:10.1371/journal.pone.0261131.

Joshi, M. L., & Kanoongo, N. (2022). Depression detection using emotional artificial intelligence and machine learning: A closer review. Materials Today: Proceedings, 58, 217–226. doi:10.1016/j.matpr.2022.01.467.

Rubaiyat, N., Apsara, A. I., Chaki, D., Arif, H., Israt, L., Kabir, L., & Rabiul Alam, M. G. (2019). Classification of depression, internet addiction and prediction of self-esteem among university students. 2019 22nd International Conference on Computer and Information Technology, ICCIT 2019, 18–20. doi:10.1109/ICCIT48885.2019.9038211.

Latubessy, A., Wardoyo, R., Musdholifah, A., & Kusrohmaniah, S. (2024). Fine tuning attribute weighted naïve Bayes model for detecting anxiety disorder levels of online gamers. International Journal of Electrical and Computer Engineering, 14(3), 3277–3286. doi:10.11591/ijece.v14i3.pp3277-3286.

Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. doi:10.1007/bf00116251.

Frénay, B., & Verleysen, M. (2014). Classification in the presence of label noise: A survey. IEEE Transactions on Neural Networks and Learning Systems, 25(5), 845–869. doi:10.1109/TNNLS.2013.2292894.

Massie, S., Craw, S., & Wiratunga, N. (2007). When similar problems don’t have similar solutions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 4626 LNAI, 92–106. doi:10.1007/978-3-540-74141-1_7.

Delany, S. J., Segata, N., & Mac Namee, B. (2012). Profiling instances in noise reduction. Knowledge-Based Systems, 31, 28–40. doi:10.1016/j.knosys.2012.01.015.

Son, S. H., & Kim, J. Y. (2006). Data reduction for instance-based learning using entropy-based partitioning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 3982 LNCS, 590–599. doi:10.1007/11751595_63.

Yang, Y., Wu, X., & Zhu, X. (2004). Dealing with predictive-but-unpredictable attributes in noisy data sources. Knowledge Discovery in Databases: PKDD 2004: 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pisa, Italy, September 20-24, 2004. Proceedings 8, 471-483. doi:10.1007/978-3-540-30116-5_43.

Brodley, C. E., & Friedl, M. A. (1999). Identifying Mislabeled Training Data. Journal of Artificial Intelligence Research, 11, 131–167. doi:10.1613/jair.606.

Siste, K., Wiguna, T., Bardasono, S., Sekartini, R., Pandelaki, J., Sarasvita, R., Suwartono, C., Murtani, B. J., Damayanti, R., Christian, H., Sen, L. T., & Nasrun, M. W. (2021). Internet addiction in adolescents: Development and validation of Internet Addiction Diagnostic Questionnaire (KDAI). Psychiatry Research, 298(71), 113829. doi:10.1016/j.psychres.2021.113829.


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DOI: 10.28991/HIJ-2025-06-01-05

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