Feature Ranking Derived from Data Mining Process (original) (raw)

Abstract

Most common feature ranking methods are based on the statistical approach. This paper compare several statistical methods with new method for feature ranking derived from data mining process. This method ranks features depending on percentage of child units that survived the selection process. A child unit is a processing element transforming the parent input features to the output. After training, units are interconnected in the feedforward hybrid neural network called GAME. The selection process is realized by means of niching genetic algorithm, where units connected to least significant features starve and fade from population. Parameters of new FR algorithm are investigated and comparison among different methods is presented on well known real world and artificial data sets.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, FEE, Czech Technical University, Prague, Czech Republic
    Aleš Pilný, Pavel Kordík & Miroslav Šnorek

Authors

  1. Aleš Pilný
  2. Pavel Kordík
  3. Miroslav Šnorek

Editor information

Véra Kůrková Roman Neruda Jan Koutník

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© 2008 Springer-Verlag Berlin Heidelberg

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Pilný, A., Kordík, P., Šnorek, M. (2008). Feature Ranking Derived from Data Mining Process. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8\_92

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