Classifier Selection and Ensemble Model for Multi-class Imbalance Learning in Education Grants Prediction

References

• Asif, R., A. Merceron, S. A. Ali, and N. G. Haider. 2017. Analyzing undergraduate students’ performance using educational data mining. Computers & Education 113:177–94. doi:10.1016/j.compedu.2017.05.007.
   Web of Science ®Google Scholar
• Baker, R. S. 2014. Educational data mining: An advance for intelligent systems in education. IEEE Intelligent Systems 29 (3):78–82. doi:10.1109/mis.2014.42.
   Web of Science ®Google Scholar
• Bi, J. J., and C. S. Zhang. 2018. An empirical comparison on state-of-the-art multi-class imbalance learning algorithms and a new diversified ensemble learning scheme. Knowledge-Based Systems 158:81–93. doi:10.1016/j.knosys.2018.05.037.
   Web of Science ®Google Scholar
• Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Cai, Z. H., D. Xu, Q. Zhang, J. X. Zhang, S. M. Ngai, and J. L. Shao. 2015. Classification of lung cancer using ensemble-based feature selection and machine learning methods. Molecular bioSystems 11 (3):791–800. doi:10.1039/c4mb00659c.
   PubMedGoogle Scholar
• Claesen, M., F. De Smet, J. A. K. Suykens, and B. De Moor. 2014. EnsembleSVM: A library for ensemble learning using support vector machines. Journal of Machine Learning Research 15:141–45.
   Web of Science ®Google Scholar
• Diez-Pastor, J. F., J. J. Rodriguez, C. I. Garcia-Osorio, and L. I. Kuncheva. 2015. Diversity techniques improve the performance of the best imbalance learning ensembles. Information Sciences 325:98–117. doi:10.1016/j.ins.2015.07.025.
   Web of Science ®Google Scholar
• Dong, X. B., Z. W. Yu, W. M. Cao, Y. F. Shi, and Q. L. Ma. 2020. A survey on ensemble learning. Frontiers of Computer Science 14 (2):241–58. doi:10.1007/s11704-019-8208-z.
   Web of Science ®Google Scholar
• Dutti, A., M. A. Ismaili, and T. Herawani. 2017. A systematic review on educational data mining. IEEE Access 5:15991–6005. doi:10.1109/access.2017.2654247.
   Web of Science ®Google Scholar
• Farid, D. M., L. Zhang, C. M. Rahman, M. A. Hossain, and R. Strachan. 2014. Hybrid decision tree and naive Bayes classifiers for multi-class classification tasks. Expert Systems with Applications 41 (4):1937–46. doi:10.1016/j.eswa.2013.08.089.
   Web of Science ®Google Scholar
• Freund, Y., and R. E. Schapire. 1996. Experiments with a new boosting algorithm. International Conference on Machine Learning 96:148–56.
   Google Scholar
• Freund, Y., and R. E. Schapire. 1999. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55 (1):119–39. doi:10.1006/jcss.1997.1504.
   Web of Science ®Google Scholar
• Friedman, J. H. 2001. Greedy function approximation: A gradient boosting machine. Annals of Statistics 29 (5):1189–232. doi:10.1214/aos/1013203451.
   Web of Science ®Google Scholar
• Gomez-Rey, P., F. Fernandez-Navarro, and E. Barbera. 2016. Ordinal regression by a gravitational model in the field of educational data mining. Expert Systems 33 (2):161–75. doi:10.1111/exsy.12138.
   Web of Science ®Google Scholar
• Guo, H. X., Y. J. Li, Y. A. Li, X. Liu, and J. L. Li. 2016. BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification. Engineering Applications of Artificial Intelligence 49:176–93. doi:10.1016/j.engappai.2015.09.011.
   Web of Science ®Google Scholar
• Guo, H. X., Y. J. Li, J. Shang, M. Y. Gu, Y. Y. Huang, and G. Bing. 2017. Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications 73:220–39. doi:10.1016/j.eswa.2016.12.035.
   Web of Science ®Google Scholar
• Heinermann, J., and O. Kramer. 2016. Machine learning ensembles for wind power prediction. Renewable Energy 89:671–79. doi:10.1016/j.renene.2015.11.073.
   Web of Science ®Google Scholar
• Krawczyk, B., L. L. Minku, J. Gama, J. Stefanowski, and M. Wozniak. 2017. Ensemble learning for data stream analysis: A survey. Information Fusion 37:132–56. doi:10.1016/j.inffus.2017.02.004.
   Web of Science ®Google Scholar
• Krawczyk, B., M. Wozniak, and G. Schaefer. 2014. Cost-sensitive decision tree ensembles for effective imbalanced classification. Applied Soft Computing 14:554–62. doi:10.1016/j.asoc.2013.08.014.
   Web of Science ®Google Scholar
• Li, X., Z. H. Yu, Z. W. Li, and N. Q. Wu. 2021. Group consensus via pinning control for a class of heterogeneous multi-agent systems with input constraints. Information Sciences 542:247–62. doi:10.1016/j.ins.2020.05.085.
   Web of Science ®Google Scholar
• Lim, P., C. K. Goh, and K. C. Tan. 2017. Evolutionary cluster-based synthetic oversampling ensemble (ECO-Ensemble) for imbalance learning. IEEE Transactions on Cybernetics 47 (9):2850–61. doi:10.1109/tcyb.2016.2579658.
   PubMed Web of Science ®Google Scholar
• Liu, J., and E. Zio. 2019. Integration of feature vector selection and support vector machine for classification of imbalanced data. Applied Soft Computing 75:702–11. doi:10.1016/j.asoc.2018.11.045.
   Web of Science ®Google Scholar
• Liu, P., P. J. Du, and K. Tan. 2014. A novel remotely sensed image classification based on ensemble learning and feature integration. Journal of Infrared and Millimeter Waves 33 (3):311–17.
   Web of Science ®Google Scholar
• Mirza, B., Z. P. Lin, and N. Liu. 2015. Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift. Neurocomputing 149:316–29. doi:10.1016/j.neucom.2014.03.075.
   Web of Science ®Google Scholar
• Pena-Ayala, A. 2014. Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications 41 (4):1432–62. doi:10.1016/j.eswa.2013.08.042.
   Web of Science ®Google Scholar
• Ramos, J. L. C., R. E. D. Silva, R. L. Rodrigues, J. C. S. Silva, and A. S. Gomes. 2016. A comparative study between clustering methods in educational data mining. IEEE Latin America Transactions 14 (8):3755–61. doi:10.1109/TLA.2016.7786360.
   Web of Science ®Google Scholar
• Santos, M. S., J. P. Soares, P. H. Abreu, H. Araujo, and J. Santos. 2018. Cross-validation for imbalanced datasets: Avoiding overoptimistic and overfitting approaches. IEEE Computational Intelligence Magazine 13 (4):59–76. doi:10.1109/mci.2018.2866730.
   Web of Science ®Google Scholar
• Sun, Y. 2019. Analysis for center deviation of circular target under perspective projection. Engineering Computations 36 (7):2403–13. doi:10.1108/EC-09-2018-0431.
   Web of Science ®Google Scholar
• Tao, X. M., Q. Li, W. J. Guo, C. Ren, C. X. Li, R. Liu, and J. R. Zou. 2019. Self-adaptive cost weights-based support vector machine cost-sensitive ensemble for imbalanced data classification. Information Sciences 487:31–56. doi:10.1016/j.ins.2019.02.062.
   Web of Science ®Google Scholar
• Wang, S., L. L. Minku, and X. Yao. 2015. Resampling-based ensemble methods for online class imbalance learning. IEEE Transactions on Knowledge and Data Engineering 27 (5):1356–68. doi:10.1109/tkde.2014.2345380.
   Web of Science ®Google Scholar
• Wozniak, M., M. Grana, and E. Corchado. 2014. A survey of multiple classifier systems as hybrid systems. Information Fusion 16:3–17. doi:10.1016/j.inffus.2013.04.006.
   Web of Science ®Google Scholar
• Yang, L., Z. H. Yu, M. A. El-Meligy, A. M. El-Sherbeeny, and N. Q. Wu. 2020. On multiplexity-aware influence spread in social networks. IEEE Access 8:106705–13. doi:10.1109/ACCESS.2020.2999312.
   Web of Science ®Google Scholar
• Yao, G., H. L. Zeng, F. Chao, C. Su, C. M. Lin, and C. L. Zhou. 2016. Integration of classifier diversity measures for feature selection-based classifier ensemble reduction. Soft Computing 20 (8):2995–3005. doi:10.1007/s00500-015-1927-7.
   Web of Science ®Google Scholar
• Yu, H. L., and J. Ni. 2014. An improved ensemble learning method for classifying high-dimensional and imbalanced biomedicine data. IEEE-ACM Transactions on Computational Biology and Bioinformatics 11 (4):657–66. doi:10.1109/tcbb.2014.2306838.
   PubMed Web of Science ®Google Scholar
• Zhang, H. T., H. L. He, and W. Y. Zhang. 2018. Classifier selection and clustering with fuzzy assignment in ensemble model for credit scoring. Neurocomputing 316:210–21. doi:10.1016/j.neucom.2018.07.070.
   Web of Science ®Google Scholar
• Zhang, J., Z. L. Li, and H. A. Li. 2019. A novel sketch-based medical 3D model retrieval approach by multi-scale weighted Gabor feature fusion and IP-HOG feature extraction. Journal of Medical Imaging and Health Informatics 9 (4):788–800. doi:10.1166/jmihi.2019.2610.
   Web of Science ®Google Scholar
• Zhou, S., Z. H. Yu, E. S. A. Nasr, H. A. Mahmoud, E. M. Awwad, and N. Q. Wu. 2020. Homomorphic encryption of supervisory control systems using automata. IEEE Access 8:147185–98. doi:10.1109/ACCESS.2020.3014217.
   Web of Science ®Google Scholar
• Zhou, Z. H. 2012. Ensemble methods: Foundations and algorithms. Horsham, PA/USA: Chapman & Hall/CRC.
   Google Scholar