| Attributes | Values |
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| rdf:type
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| Description
| - Nowadays a Feature Ranking (FR) is commonly used method for obtaining information about a large data sets with various dimensionality. This knowledge can be used in a next step of data processing. Accuracy and a speed of experiments can be improved by this. Our approach is based on Artificial Neural Networks (ANN) instead of classical statistical methods. We obtain the knowledge as a by-product of Niching Genetic Algorithm (NGA) used for creation of a feedforward hybrid neural network called GAME. In this paper we present a behaviour of FeRaNGA (Feature Ranking method using Niching Genetic Algorithm(NGA)) during a learning process, especially in every layer of generated GAME network. We want to answer how important is NGA configuration and processing procedure for FR results because behaviour of GA is nondeterministic and thereby were results of FeRaNGA also indefinitive. This method ranks features depending on a percentage of processing elements that survived a selection process.
- Nowadays a Feature Ranking (FR) is commonly used method for obtaining information about a large data sets with various dimensionality. This knowledge can be used in a next step of data processing. Accuracy and a speed of experiments can be improved by this. Our approach is based on Artificial Neural Networks (ANN) instead of classical statistical methods. We obtain the knowledge as a by-product of Niching Genetic Algorithm (NGA) used for creation of a feedforward hybrid neural network called GAME. In this paper we present a behaviour of FeRaNGA (Feature Ranking method using Niching Genetic Algorithm(NGA)) during a learning process, especially in every layer of generated GAME network. We want to answer how important is NGA configuration and processing procedure for FR results because behaviour of GA is nondeterministic and thereby were results of FeRaNGA also indefinitive. This method ranks features depending on a percentage of processing elements that survived a selection process. (en)
- Nowadays a Feature Ranking (FR) is commonly used method for obtaining information about a large data sets with various dimensionality. This knowledge can be used in a next step of data processing. Accuracy and a speed of experiments can be improved by this. Our approach is based on Artificial Neural Networks (ANN) instead of classical statistical methods. We obtain the knowledge as a by-product of Niching Genetic Algorithm (NGA) used for creation of a feedforward hybrid neural network called GAME. In this paper we present a behaviour of FeRaNGA (Feature Ranking method using Niching Genetic Algorithm(NGA)) during a learning process, especially in every layer of generated GAME network. We want to answer how important is NGA configuration and processing procedure for FR results because behaviour of GA is nondeterministic and thereby were results of FeRaNGA also indefinitive. This method ranks features depending on a percentage of processing elements that survived a selection process. (cs)
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| Title
| - Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling
- Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling (en)
- Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling (cs)
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| skos:prefLabel
| - Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling
- Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling (en)
- Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling (cs)
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| skos:notation
| - RIV/68407700:21230/08:03145893!RIV09-MSM-21230___
|
| http://linked.open...avai/riv/aktivita
| |
| http://linked.open...avai/riv/aktivity
| - P(KJB201210701), Z(MSM6840770012)
|
| http://linked.open...vai/riv/dodaniDat
| |
| http://linked.open...aciTvurceVysledku
| |
| http://linked.open.../riv/druhVysledku
| |
| http://linked.open...iv/duvernostUdaju
| |
| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
| |
| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/08:03145893
|
| http://linked.open...riv/jazykVysledku
| |
| http://linked.open.../riv/klicovaSlova
| - Inductive modelling, Feature Ranking, Artificial Neural Networks, FeRaNGA, Nitching Genetic Algorith (en)
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| http://linked.open.../riv/klicoveSlovo
| |
| http://linked.open...ontrolniKodProRIV
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| http://linked.open...v/mistoKonaniAkce
| |
| http://linked.open...i/riv/mistoVydani
| |
| http://linked.open...i/riv/nazevZdroje
| - Proceedings of the 2nd International Conference on Inductive Modelling
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| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
| |
| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...vavai/riv/projekt
| |
| http://linked.open...UplatneniVysledku
| |
| http://linked.open...iv/tvurceVysledku
| - Pilný, Aleš
- Kordík, Pavel
- Šnorek, Miroslav
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| http://linked.open...vavai/riv/typAkce
| |
| http://linked.open.../riv/zahajeniAkce
| |
| http://linked.open...n/vavai/riv/zamer
| |
| number of pages
| |
| http://purl.org/ne...btex#hasPublisher
| |
| https://schema.org/isbn
| |
| http://localhost/t...ganizacniJednotka
| |
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