Algorithms for Attribute Selection and Knowledge Discovery

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 731)


The features relevant selection is a task performed prior to the data mining and can be seen as one of the most important problems to solve in the data preprocessing stage an in the machine learning. With the feature selection is mainly intended to improve predictive or descriptive performance of models and implement faster and less expensive algorithms. In this paper an analysis about feature selection methods is made emphasizing on decision trees, entropy measure for ranking features, and estimation of distribution algorithms. Finally, we show the result analysis of execute the three algorithms.


Features selection Complexity Decision trees Entropy Estimation of distribution algorithms Machine learning Data mining 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.District University “Francisco José de Caldas”BogotáColombia

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