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The application of active learning in identification of students with financial difficulties

Published: 20 December 2017 Publication History
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    The previous classifiers tend to achieve unsatisfied performance with class-imbalanced data. In order to identify the poverty students using data with few labels, we adopted the active learning method. The results show that it can get lower error compared with the random sampling strategy, which means that the strategy applied in our problem is effective. With the strategy, we can classify the poverty students with much more accuracy, which is useful in distributing subsidies to students in college.

    References

    [1]
    Francisco Charte, Antonio J. Rivera, María J. del Jesus, Francisco Herrera, Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets, Neurocomputing
    [2]
    Settles, B. (2009). Active learning literature survey. University of Wisconsinmadison, 39(2), 127--131.
    [3]
    Nikulin, V., Mclachlan, G. J., & Shu, K. N. (2009). Ensemble Approach for the Classification of Imbalanced Data. Australasian Joint Conference on Advances in Artificial Intelligence,5866, 291--300.
    [4]
    N. Japkowicz, in: Proceedings of the AAAI'2000 Workshop on Learning from Imbalanced Data Sets, AAAI Tech Report WS-00-05, AAAI, 2000.
    [5]
    N.V. Chawla, N. Japkowicz, A. Kotcz, in: Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Data Sets, ICML, 2003.
    [6]
    Yanmin S., Andrew K. C. Wong, & Mohamed S. Kamel. (2009). Classification of imbalanced data: a review. International Journal of Pattern Recognition and Artificial Intelligence, 23(04), 687--719.
    [7]
    Estabrooks, A., Jo, T., & Japkowicz, N. (2010). A multiple resampling method for learning from imbalanced data sets. Computational Intelligence, 20(1), 18--36.
    [8]
    Haibo He, & Edwardo A. Garcia. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263--1284.
    [9]
    Estabrooks, A. (2000). A combination scheme for inductive learning from imbalanced data sets. Ai Magazine.
    [10]
    Liu, X. Y., & Zhou, Z. H. (2006). The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study. International Conference on Data Mining (pp. 970--974). IEEE.
    [11]
    Sun, Y., Kamel, M. S., Wong, A. K. C., & Wang, Y. (2007). Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition, 40(12), 3358--3378.
    [12]
    D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, pages 3--12. ACM/Springer, 1994
    [13]
    Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. The Workshop on Computational Learning Theory,284, 287--294. ACM.
    [14]
    D. A. Cohn, Z. Ghahramani, & M. I. Jordan. (1995). Active learning with statistical models. Journal of Artificial Intelligence Research, 4(1), 705--712.
    [15]
    V. Vapnik. (1998). Statistical Learning Theory. John Wiley & Sons, New York.
    [16]
    Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery. 2(2): 121--167.
    [17]
    Gonzalez-Abril, L., Angulo, C., Nuñez, H., & Leal, Y. (2017). Handling binary classification problems with a priority class by using support vector machines. Applied Soft Computing. 61, 661--669.

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    • (2024)Active-Learning Method: An Effective Way to Generate Ground Truth Data to Test & Validate ADAS Function DevelopmentSAE Technical Paper Series10.4271/2024-26-0364Online publication date: 23-Jan-2024

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      cover image ACM Other conferences
      ICETC '17: Proceedings of the 9th International Conference on Education Technology and Computers
      December 2017
      270 pages
      ISBN:9781450354356
      DOI:10.1145/3175536
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      Published: 20 December 2017

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      Author Tags

      1. Active Learning
      2. imbalanced classification
      3. poverty student identification

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      • (2024)Active-Learning Method: An Effective Way to Generate Ground Truth Data to Test & Validate ADAS Function DevelopmentSAE Technical Paper Series10.4271/2024-26-0364Online publication date: 23-Jan-2024

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