Feature Ranking Derived from Data Mining Process

Authors:
Aleš Pilný;Pavel Kordík;Miroslav Šnorek
Affiliations:
Department of Computer Science and Engineering, FEE, Czech Technical University, Prague, Czech Republic;Department of Computer Science and Engineering, FEE, Czech Technical University, Prague, Czech Republic;Department of Computer Science and Engineering, FEE, Czech Technical University, Prague, Czech Republic
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Year:
2008

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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.