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Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data

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Abstract

Predicting student failure at school has become a difficult challenge due to both the high number of factors that can affect the low performance of students and the imbalanced nature of these types of datasets. In this paper, a genetic programming algorithm and different data mining approaches are proposed for solving these problems using real data about 670 high school students from Zacatecas, Mexico. Firstly, we select the best attributes in order to resolve the problem of high dimensionality. Then, rebalancing of data and cost sensitive classification have been applied in order to resolve the problem of classifying imbalanced data. We also propose to use a genetic programming model versus different white box techniques in order to obtain both more comprehensible and accuracy classification rules. The outcomes of each approach are shown and compared in order to select the best to improve classification accuracy, specifically with regard to which students might fail.

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Acknowledgements

This research has been supported by projects of the Regional Government of Andalucía and the Ministry of Science and Technology, P08-TIC-3720, TIN-2011-22408, FPU grant AP2010-0042 and FEDER funds.

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Authors and Affiliations

  1. Autonomous University of Zacatecas, Zacatecas, México

    Carlos Márquez-Vera

  2. Department of Computer Science, University of Córdoba, Córdoba, Spain

    Alberto Cano, Cristóbal Romero & Sebastián Ventura

Authors
  1. Carlos Márquez-Vera
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  2. Alberto Cano
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  3. Cristóbal Romero
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  4. Sebastián Ventura
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Correspondence to Cristóbal Romero.

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Márquez-Vera, C., Cano, A., Romero, C. et al. Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Appl Intell 38, 315–330 (2013). https://doi.org/10.1007/s10489-012-0374-8

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