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

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@Article{Marquez-Vera:2013:AI,

• author = "Carlos Marquez-Vera and Alberto Cano and Cristobal Romero and Sebastian Ventura",
• title = "Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data",
• journal = "Applied Intelligence",
• year = "2013",
• volume = "38",
• number = "3",
• pages = "315--330",
• month = apr,
• keywords = "genetic algorithms, genetic programming, educational data mining, Predicting student performance, Classification, Educational data mining, Student failure, Grammar-based genetic programming",
• language = "English",
• publisher = "Springer",
• ISSN = "0924-669X",
• DOI = "doi:10.1007/s10489-012-0374-8",
• size = "16 pages",
• 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.",
• notes = "Also known as \cite{Marquez:2013:AI}",

}

Genetic Programming entries for Carlos Marquez-Vera Alberto Cano Rojas Cristobal Romero Morales Sebastian Ventura

Citations