EXPLORING COMPLEX DECISION TREES: UNVEILING DATA PATTERNS AND OPTIMAL PREDICTIVE POWER
Keywords:
Accuracy, Decision tree models, Dropout, Preventative measure, Pruning
Abstract
This research investigates the development and analysis of decision tree models in the context of classification tasks. Decision tree models were developed without employing pruning or pre-pruning techniques and were tested on relevant datasets. The research findings demonstrate that complex models without pruning achieved the highest level of accuracy in classifying data. This study was inspired by the potential issue of students facing the risk of not completing their studies (dropout), which could lead to a decline in the college's accreditation rating. Therefore, this model was devised to assist in identifying factors that could influence this outcome as a preventative measure. Additionally, we successfully generated clear visualizations of the decision trees, enhancing the understanding of the model's decision-making process. This research provides insights into the adaptability of decision tree models within this specific case and showcases their potential for enhancing decision-making across various contexts. These findings encourage further discussions on the benefits of pruning methods within this specific context and the broader application potential of decision tree models.
References
Aronov, B., de Berg, M., Cardinal, J., Ezra, E., Iacono, J., & Sharir, M. (2023). Subquadratic algorithms for some 3Sum-hard geometric problems in the algebraic decision-tree model. Computational Geometry, 109, 101945. https://doi.org/https://doi.org/10.1016/j.comgeo.2022.101945
Cao, Y., Zhao, H., Liang, G., Zhao, J., Liao, H., & Yang, C. (2023). Fast and explainable warm-start point learning for AC Optimal Power Flow using decision tree. International Journal of Electrical Power & Energy Systems, 153, 109369. https://doi.org/https://doi.org/10.1016/j.ijepes.2023.109369
Custode, L. L., Mento, F., Tursi, F., Smargiassi, A., Inchingolo, R., Perrone, T., Demi, L., & Iacca, G. (2023). Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees. Applied Soft Computing, 133, 109926. https://doi.org/https://doi.org/10.1016/j.asoc.2022.109926
Dimovski, A. S. (2023). Quantitative program sketching using decision tree-based lifted analysis. Journal of Computer Languages, 75, 101206. https://doi.org/https://doi.org/10.1016/j.cola.2023.101206
Gifford, M., & Bayrak, T. (2023). A predictive analytics model for forecasting outcomes in the National Football League games using decision tree and logistic regression. Decision Analytics Journal, 8, 100296. https://doi.org/https://doi.org/10.1016/j.dajour.2023.100296
Han, X., Zhu, X., Pedrycz, W., & Li, Z. (2023). A three-way classification with fuzzy decision trees. Applied Soft Computing, 132, 109788. https://doi.org/https://doi.org/10.1016/j.asoc.2022.109788
Huang, X., Zhou, F., Niu, W., Li, T., Lu, Y., Zhou, Y., Yin, H., & Yan, C. (2023). Multi-stage affine motion estimation fast algorithm for versatile video coding using decision tree. Journal of Visual Communication and Image Representation, 96, 103910. https://doi.org/https://doi.org/10.1016/j.jvcir.2023.103910
Li, Y., Feng, Y., & Qian, Q. (2023). FDPBoost: Federated differential privacy gradient boosting decision trees. Journal of Information Security and Applications, 74, 103468. https://doi.org/https://doi.org/10.1016/j.jisa.2023.103468
Loyola-González, O., Ramírez-Sáyago, E., & Medina-Pérez, M. A. (2023). Towards improving decision tree induction by combining split evaluation measures. Knowledge-Based Systems, 277, 110832. https://doi.org/https://doi.org/10.1016/j.knosys.2023.110832
Mariano, A. M., Ferreira, A. B. de M. L., Santos, M. R., Castilho, M. L., & Bastos, A. C. F. L. C. (2022). Decision trees for predicting dropout in Engineering Course students in Brazil. Procedia Computer Science, 214, 1113–1120. https://doi.org/https://doi.org/10.1016/j.procs.2022.11.285
Martins, J. (2019). Assessing the success behind the use of education management information systems in higher education. Telematics and Informatics, 38, 182–193. https://doi.org/10.1016/j.tele.2018.10.001
Portoleau, T., Artigues, C., & Guillaume, R. (2023). Robust decision trees for the multi-mode project scheduling problem with a resource investment objective and uncertain activity duration. European Journal of Operational Research. https://doi.org/https://doi.org/10.1016/j.ejor.2023.07.035
Puthal, D., Wilson, S., Nanda, A., Liu, M., Swain, S., Sahoo, B. P. S., Yelamarthi, K., Pillai, P., El-Sayed, H., & Prasad, M. (2022). Decision tree based user-centric security solution for critical IoT infrastructure. Computers and Electrical Engineering, 99, 107754. https://doi.org/https://doi.org/10.1016/j.compeleceng.2022.107754
Rong, L. J. (2022). Distance learning quality assessment of universities based on interval monotonic decision tree algorithm. Computers and Electrical Engineering, 102, 108116. https://doi.org/https://doi.org/10.1016/j.compeleceng.2022.108116
Salamate, F. ezzahra, & Zahi, J. (2022). Supervised learning: classification using decision trees for better practice in epidemiology Case study: The prevalence of tuberculosis. Procedia Computer Science, 201, 783–788. https://doi.org/https://doi.org/10.1016/j.procs.2022.03.107
Shanbehzadeh, M., Nopour, R., & Kazemi-Arpanahi, H. (2022). Using decision tree algorithms for estimating ICU admission of COVID-19 patients. Informatics in Medicine Unlocked, 30, 100919. https://doi.org/https://doi.org/10.1016/j.imu.2022.100919
Xu, X., Wang, J., Peng, H., & Wu, R. (2019). Prediction of academic performance associated with internet usage behaviors using machine learning algorithms. Computers in Human Behavior, 98, 166–173. https://doi.org/https://doi.org/10.1016/j.chb.2019.04.015
Cao, Y., Zhao, H., Liang, G., Zhao, J., Liao, H., & Yang, C. (2023). Fast and explainable warm-start point learning for AC Optimal Power Flow using decision tree. International Journal of Electrical Power & Energy Systems, 153, 109369. https://doi.org/https://doi.org/10.1016/j.ijepes.2023.109369
Custode, L. L., Mento, F., Tursi, F., Smargiassi, A., Inchingolo, R., Perrone, T., Demi, L., & Iacca, G. (2023). Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees. Applied Soft Computing, 133, 109926. https://doi.org/https://doi.org/10.1016/j.asoc.2022.109926
Dimovski, A. S. (2023). Quantitative program sketching using decision tree-based lifted analysis. Journal of Computer Languages, 75, 101206. https://doi.org/https://doi.org/10.1016/j.cola.2023.101206
Gifford, M., & Bayrak, T. (2023). A predictive analytics model for forecasting outcomes in the National Football League games using decision tree and logistic regression. Decision Analytics Journal, 8, 100296. https://doi.org/https://doi.org/10.1016/j.dajour.2023.100296
Han, X., Zhu, X., Pedrycz, W., & Li, Z. (2023). A three-way classification with fuzzy decision trees. Applied Soft Computing, 132, 109788. https://doi.org/https://doi.org/10.1016/j.asoc.2022.109788
Huang, X., Zhou, F., Niu, W., Li, T., Lu, Y., Zhou, Y., Yin, H., & Yan, C. (2023). Multi-stage affine motion estimation fast algorithm for versatile video coding using decision tree. Journal of Visual Communication and Image Representation, 96, 103910. https://doi.org/https://doi.org/10.1016/j.jvcir.2023.103910
Li, Y., Feng, Y., & Qian, Q. (2023). FDPBoost: Federated differential privacy gradient boosting decision trees. Journal of Information Security and Applications, 74, 103468. https://doi.org/https://doi.org/10.1016/j.jisa.2023.103468
Loyola-González, O., Ramírez-Sáyago, E., & Medina-Pérez, M. A. (2023). Towards improving decision tree induction by combining split evaluation measures. Knowledge-Based Systems, 277, 110832. https://doi.org/https://doi.org/10.1016/j.knosys.2023.110832
Mariano, A. M., Ferreira, A. B. de M. L., Santos, M. R., Castilho, M. L., & Bastos, A. C. F. L. C. (2022). Decision trees for predicting dropout in Engineering Course students in Brazil. Procedia Computer Science, 214, 1113–1120. https://doi.org/https://doi.org/10.1016/j.procs.2022.11.285
Martins, J. (2019). Assessing the success behind the use of education management information systems in higher education. Telematics and Informatics, 38, 182–193. https://doi.org/10.1016/j.tele.2018.10.001
Portoleau, T., Artigues, C., & Guillaume, R. (2023). Robust decision trees for the multi-mode project scheduling problem with a resource investment objective and uncertain activity duration. European Journal of Operational Research. https://doi.org/https://doi.org/10.1016/j.ejor.2023.07.035
Puthal, D., Wilson, S., Nanda, A., Liu, M., Swain, S., Sahoo, B. P. S., Yelamarthi, K., Pillai, P., El-Sayed, H., & Prasad, M. (2022). Decision tree based user-centric security solution for critical IoT infrastructure. Computers and Electrical Engineering, 99, 107754. https://doi.org/https://doi.org/10.1016/j.compeleceng.2022.107754
Rong, L. J. (2022). Distance learning quality assessment of universities based on interval monotonic decision tree algorithm. Computers and Electrical Engineering, 102, 108116. https://doi.org/https://doi.org/10.1016/j.compeleceng.2022.108116
Salamate, F. ezzahra, & Zahi, J. (2022). Supervised learning: classification using decision trees for better practice in epidemiology Case study: The prevalence of tuberculosis. Procedia Computer Science, 201, 783–788. https://doi.org/https://doi.org/10.1016/j.procs.2022.03.107
Shanbehzadeh, M., Nopour, R., & Kazemi-Arpanahi, H. (2022). Using decision tree algorithms for estimating ICU admission of COVID-19 patients. Informatics in Medicine Unlocked, 30, 100919. https://doi.org/https://doi.org/10.1016/j.imu.2022.100919
Xu, X., Wang, J., Peng, H., & Wu, R. (2019). Prediction of academic performance associated with internet usage behaviors using machine learning algorithms. Computers in Human Behavior, 98, 166–173. https://doi.org/https://doi.org/10.1016/j.chb.2019.04.015
Published
2023-09-05
How to Cite
Setiawan, I., Fina Antika Cahyani, R., & Sadida, I. (2023). EXPLORING COMPLEX DECISION TREES: UNVEILING DATA PATTERNS AND OPTIMAL PREDICTIVE POWER. Journal of Innovation And Future Technology (IFTECH), 5(2), 112-123. https://doi.org/10.47080/iftech.v5i2.2829
1.jpg)
