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Feature Selection Methods Based on Decision Rule and Tree Models

  • Wiesław Paja
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 57)

Abstract

Feature selection methods, as a preprocessing step to machine learning, is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this work, a novel concepts of relevant feature selection based on information gathered from decision rule and decision tree models were introduced. A new measures DRQualityImp and DTLevelImp were additionally defined. The first one is based on feature presence frequency and rule quality, while the second is based on feature presence on different levels inside decision tree. The efficiency and effectiveness of that method is demonstrated through the exemplary use of five real-world datasets. Promising initial results of classification efficiency could be gained together with substantial reduction of problem dimensionality.

Keywords

Feature selection Feature ranking Decision rules Dimensionality reduction Relevance and irrelevance 

Notes

Acknowledgments

This work was supported by the Center for Innovation and Transfer of Natural Sciences and Engineering Knowledge at the University of Rzesz̀w.

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© Springer International Publishing Switzerland 2016

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Authors and Affiliations

  1. 1.Faculty of Mathematics and Natural Sciences, Department of Computer ScienceUniversity of RzeszówRzeszówPoland

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