Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03145893" target="_blank" >RIV/68407700:21230/08:03145893 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling

Original language 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.

Czech name

Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling

Czech 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.

Type

D - Article in proceedings

CEP classification

IN - Informatics

OECD FORD branch

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Project

<a href="/en/project/KJB201210701" target="_blank" >KJB201210701: Automated Knowledge Extraction</a><br>

Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2008

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 2nd International Conference on Inductive Modelling

ISBN

978-966-02-4889-2

ISSN

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e-ISSN

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Number of pages

5

Pages from-to

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Publisher name

Ukr. INTEI

Place of publication

Kiev

Event location

Kyjev

Event date

Sep 15, 2008

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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