Feature Selection for Data and Pattern Recognition

Volume 584 of the series Studies in Computational Intelligence pp 29-44


Feature Evaluation by Filter, Wrapper, and Embedded Approaches

  • Urszula StańczykAffiliated withInstitute of Informatics, Silesian University of Technology Email author 

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The choice of particular variables for construction of a set of characteristic features relevant to classification can be executed in a kind of external process with respect to a classification system employed in pattern recognition, it can depend on the performance of such system, or it can involve some inherent mechanism, build-in in the system. The three types of approaches correspond to three categories of methodologies typically exploited in feature selection and reduction: filters, wrappers, and embedded solutions, respectively. They are used when domain knowledge is unavailable or insufficient for an informed choice, or in order to support this expert knowledge to achieve higher efficiency, enhanced classification, or reduced sizes of classifiers. The chapter illustrates the combinations of the three approaches with the aim of feature evaluation, for binary classification with balanced, for the task of authorship attribution that belongs with stylometric analysis of texts.


Feature evaluation Filter Wrapper Embedded solution DRSA ANN Stylometry Authorship attribution