A Study of Measurement Modeling of Decision Trees in Machine Learning Processes

[1] Li, F., Ma, S., Feng, Y., & Jin, C. (2022). Research on data consistency detection method based on interactive matching under sampling background. Knowledge-based systems. Search in Google Scholar

[2] Sanchez-Lopez, J. E., Javier Solís-García, & Riquelme, J. C. (2024). Semi-real-time decision tree ensemble algorithms for very short-term solar irradiance forecasting. International Journal of Electrical Power and Energy Systems, 158. Search in Google Scholar

[3] Mutlu, N. G., & Altuntas, S. (2023). Analyzing factors influencing the severity of occupational accidents in textile industry using decision tree algorithms. Cluster Computing, 1-39. Search in Google Scholar

[4] Monteiro, L. B., Ribeiro, V., Garcia, C. P., Filho, G. P. R., & Weigang, L. (2023). 4d trajectory conflict detection and resolution using decision tree pruning method. IEEE Latin America Transactions, 21, 277-287. Search in Google Scholar

[5] Nagarajan, V., Basu, R., & Ganguly, S. (2022). A binomial decision tree to manage yield‐uncertainty in multi‐round academic admissions processes. Naval Research Logistics (NRL), 69(2), 303-319. Search in Google Scholar

[6] Han, Y., Liu, C., Yan, L., & Ren, L. (2021). Design of decision tree structure with improved bpnn nodes for high-accuracy locomotion mode recognition using a single imu. Sensors, 21(2), 526. Search in Google Scholar

[7] Xiaoyong, Gao, Dexian, Huang, Yongheng, & Jiang, et al. (2018). A decision tree based decomposition method for oil refinery scheduling. Chinese Journal of Chemical Engineering, v.26(08), 13-20. Search in Google Scholar

[8] Fazlollahtabar, H., & Niaki, S. T. A. (2017). Integration of fault tree analysis, reliability block diagram and hazard decision tree for industrial robot reliability evaluation. Industrial Robot, 44(6), 754-764. Search in Google Scholar

[9] Maolin, S., Qi, G., Liye, L., & Shenglei, Z. (2023). A decision tree-assisted polynomial regression model with application in the cutting force analysis of cutters of a tunnel boring machine. Engineering Optimization. Search in Google Scholar

[10] Hsu, C. H. (2021). Optimal decision tree for cycle time prediction and allowance determination. IEEE Access, PP(99), 1-1. Search in Google Scholar

[11] Giacoumidis, E., Matin, A., Wei, J., Doran, N. J., Barry, L. P., & Wang, X. (2018). Blind nonlinearity equalization by machine-learning-based clustering for single- and multichannel coherent optical ofdm. Journal of Lightwave Technology. Search in Google Scholar

[12] Zhang, Yao, Saxe, Andrew, M., & Advani, et al. (2018). Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning. Molecular Physics. Search in Google Scholar

[13] Tongtong, W., Zhiqiang, X., & Zhigang, L. (2017). Performance evaluation of machine learning methods for leaf area index retrieval from time-series modis reflectance data. Sensors (Basel, Switzerland), 17(1). Search in Google Scholar

[14] Xuan, H., & Quynh, N. (2018). Botnet detection based on machine learning techniques using dns query data. Future Internet, 10(5), 43-. Search in Google Scholar

[15] lvaro Cortés‐Molino, Alejandra Valdés‐Uribe, Ellser, F., Bulusu, M., Ahongshangbam, J., & Hendrayanto, et al. (2024). Combining uav thermography, point cloud analysis and machine learning for assessing small‐scale evapotranspiration patterns in a tropical rainforest. Ecohydrology(1), 17. Search in Google Scholar

[16] Swhli, K. M. H., Jovic, S., Arsic, N., & Spalevic, P. (2017). Detection and evaluation of heating load of building by machine learning. Sensor Review, 38(1), 99-101. Search in Google Scholar

[17] Yu, M. D., Hiller, M., Delvaux, J., Sowell, R., & Verbauwhede, I. (2017). A lockdown technique to prevent machine learning on pufs for lightweight authentication. IEEE Transactions on Multi-Scale Computing Systems, 2(3), 146-159. Search in Google Scholar

[18] Grisafi, A., Wilkins, D. M., Csányi, Gábor, & Ceriotti, M. (2018). Symmetry-adapted machine learning for tensorial properties of atomistic systems. Physical Review Letters, 120(3), 036002. Search in Google Scholar

[19] Cao, Xianghui, Rui, Liu, Lu, & Cheng, et al. (2018). A machine learning-based algorithm for joint scheduling and power control in wireless networks. IEEE Internet of Things Journal, 5(6), 4308-4318. Search in Google Scholar

[20] HernándezVíctor Adrián Sosa, MonroyRaúl, Medina-PérezMiguel Angel, Loyola-GonzálezOctavio, & HerreraFrancisco. (2021). A practical tutorial for decision tree induction. ACM Computing Surveys (CSUR). Search in Google Scholar

[21] Li, Z., Wang, Y., & Wang, F. (2018). A study on fast calling variants from next-generation sequencing data using decision tree. Bmc Bioinformatics, 19(1), 145. Search in Google Scholar

[22] Cherfi, A., Nouira, K., & Ferchichi, A. (2018). Very fast c4.5 decision tree algorithm. Applied Artificial Intelligence, 32(1-3), 119-137. Search in Google Scholar

[23] Saettler, Laber, Pereira, & FDM. (2017). Decision tree classification with bounded number of errors. INFORM PROCESS LETT. Search in Google Scholar

[24] Sampath, A. K., Gomathi, D. N., Engineering, R. C. O., & University, V. R. S. R. T. (2017). Decision tree and deep learning based probabilistic model for character recognition. Journal of Central South University. Search in Google Scholar

[25] Fontoura, L. C. M. M., Lins, H. W. C., Bertuleza, A. S., D’Assuncao, A. G., & Neto, A. G. (2021). Synthesis of multiband frequency selective surfaces using machine learning with the decision tree algorithm. IEEE Access, PP(99), 1-1. Search in Google Scholar

[26] Gillis, J. M., & Morsi, W. G. (2017). Non-intrusive load monitoring using semi-supervised machine learning and wavelet design. IEEE Transactions on Smart Grid. Search in Google Scholar

[27] Fenglei, Fa, Wenxiang, Cong, Ge, & Wan. (2017). A new type of neurons for machine learning. International Journal for Numerical Methods in Biomedical Engineering. Search in Google Scholar

[28] Schneider, D. (2017). Deeper and cheaper machine learning [top tech 2017]. IEEE Spectrum, 54(1), 42-43. Search in Google Scholar

[29] Usama, M., Qadir, J., Raza, A., Arif, H., Yau, K. L. A., & Elkhatib, Y., et al. (2017). Unsupervised machine learning for networking: techniques, applications and research challenges. IEEE Access, PP(99). Search in Google Scholar

[30] Wang, S., Jia, Z., & Cao, N. (2022). Research on optimization and application of spark decision tree algorithm under cloud-edge collaboration. International journal of intelligent systems. Search in Google Scholar

[31] Shah, S. A. R., & Issac, B. (2017). Performance comparison of intrusion detection systems and application of machine learning to snort system. Future Generation Computer Systems, 80, 157-170. Search in Google Scholar