Abstract. Decision tree learning algorithms have been successfully used in knowledge discovery. They use induction in order to provide an appropriate classification of objects in terms of their attributes, inferring decision tree rules. This paper reports on the use of ID3 to Web attack detection. Even though simple, ID3 is sufficient to put apart a number of Web attacks, including a large proportion of their variants. It also surpasses existing methods: it portrays a higher true-positive detection rate and a lower false-positive one. The ID3 output classification rules that are easy to read and so computer officers are more likely to grasp the root of an attack, as well as extending the capabilities of the classifier.

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Resumen. En este artículo, se propone el uso de un ensamble de clasificadores para determinar el perfil académico del estudiante, basado en su promedio general y en datos relacionados a los factores educativos: actividades de estudio, formas de aprendizaje y hábitos de estudio. Los datos usados se obtuvieron del cuestionario socioeconómico aplicado a los estudiantes del Centro Universitario UAEM Valle de México, asignándole la clase correspondiente de acuerdo con su promedio general. Las clases se definieron como excelente, bueno y regular. Para cada grupo de factores, se utilizó el algoritmo C4.5 para generar el clasificador correspondiente. El ensamble de clasificadores fue entonces diseñado utilizando una red neuronal artificial. La red neuronal recibe como entrada la clasificación asignada por los tres clasificadores y es entrenada para asignar la clase correcta usando un subconjunto de los datos. Se observa en los resultados que el ensamble propuesto tiene mejor desempeño comparado con los clasificadores independientes.Palabras clave: Ensamble de clasificadores, árboles de decisión, redes neuronales artificiales.Abstract. In this paper, an ensemble of classifiers is proposed to determine student academic profile using decision trees and neural networks, based on his grade point average and data related to educative factors: study activities, learning forms and study habits. The data came from a socioeconomic questionnaire applied to the students of the University Center UAEM Valley of Mexico, and the corresponding class was assigned based on his grade point average. The classes were defined as: excellent, good, and regular. For each group of factors, the C4.5 algorithm was applied to build the corresponding 255