. "Prague" . "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."@en . "10"^^ . "3"^^ . . . "\u0160norek, Miroslav" . "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." . "Heidelberg" . "3"^^ . . "Artificial Neural Networks - ICANN 2008" . "[9593DB06DBA6]" . "Piln\u00FD, Ale\u0161" . "RIV/68407700:21230/08:03145492!RIV09-MSM-21230___" . . . . "978-3-540-87558-1" . . "P(KJB201210701), Z(MSM6840770012)" . "RIV/68407700:21230/08:03145492" . . "Feature Ranking Derived from Data Mining Process"@cs . "2008-09-03+02:00"^^ . . . . . "0302-9743" . "Feature Ranking, FAKE-GAME, Niching Genetic Algorithm, Median, Artificial Neural Network"@en . "Feature Ranking Derived from Data Mining Process"@en . "Feature Ranking Derived from Data Mining Process" . "21230" . . "Feature Ranking Derived from Data Mining Process"@cs . . . "Feature Ranking Derived from Data Mining Process" . . "000259567200092" . . . "Feature Ranking Derived from Data Mining Process"@en . "Springer-Verlag" . . "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."@cs . "367780" . "Kord\u00EDk, Pavel" . . .