About site: Artificial Intelligence/Machine Learning/Software - KEEL: Knowledge Extraction based on Evolutionary Learning
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Title: Artificial Intelligence/Machine Learning/Software - KEEL: Knowledge Extraction based on Evolutionary Learning The aim of this project is to develop a Computational Environment for integrating the design and use of knowledge extraction models from data using evolutionary algorithms. Genetic learning may also
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KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clustering, pattern mining and so on) main main KEEL Description Members KEEL Blog KEEL Publications List of References by Specific Areas KEEL-dataset KEEL Implemented Algorithms KEEL Software Tool JCLEC Links KEEL Workshops Identification Access   Ministerio Visits since 05/11/2005: 17323 Graphs   Download KEEL Software KEEL is a software tool to assess evolutionary algorithms for Data Mining problems including regression, classification, clustering, pattern mining and so on. It contains a big collection of classical knowledge extraction algorithms, preprocessing techniques (instance selection, feature selection, discretization, imputation methods for missing values, etc.), Computational Intelligence based learning algorithms, including evolutionary rule learning algorithms based on different approaches (Pittsburgh, Michigan and IRL, ...), and hybrid models such as genetic fuzzy systems, evolutionary neural networks, etc. It allows us to perform a complete analysis of any learning model in comparison to existing ones, including a statistical test module for comparison. Moreover, KEEL has been designed with a double goal: research and educational. For a detailed description, see the section 'KEEL description' on the left menu. KEEL description paper: J. Alcalá-Fdez, L. Sánchez, S. García, M.J. del Jesus, S. Ventura, J.M. Garrell, J. Otero, C. Romero, J. Bacardit, V.M. Rivas, J.C. Fernández, F. Herrera. KEEL: A Software Tool to Assess Evolutionary Algorithms to Data Mining Problems. Soft Computing (2008) In press, doi: 10.1007/s00500-008-0323-y.   Pdf KEEL: Knowledge Extraction based on Evolutionary Learning (Spanish National Project TIC2002-04036-C05 and TIN2005-08386-C05) This Project is being developed with the collaboration of five Research Groups: sci2s SCI2S (Spanish National Proyect TIC2002-04036-C05-01 and Spanish National Proyect TIN2005-08386-C05-01) ayrna Ayrna (Spanish National Proyect TIC2002-04036-C05-02 and Spanish National Proyect TIN2005-08386-C05-02) grsi GRSI (Spanish National Proyect TIC2002-04036-C05-03 and Spanish National Proyect TIN2005-08386-C05-04) si Intelligent Systems (Spanish National Proyect TIC2002-04036-C05-04 and Spanish National Proyect TIN2005-08386-C05-03) mm Metrology and Models (Spanish National Proyect TIC2002-04036-C05-05 and Spanish National Proyect TIN2005-08386-C05-05) Content RSS KEEL Blog Copyright 2004-2008,KEEL (Knowledge Extraction based on Evolutionary Learning)About the Webmaster TeamValid XHTML 1.1 CSS Valido!  _uacct = "UA-366771-5";urchinTracker();
 

The

aim

of

this

project

is

to

develop

a

Computational

Environment

for

integrating

the

design

and

use

of

knowledge

extraction

models

from

data

using

evolutionary

algorithms.

Genetic

learning

may

also

http://sci2s.ugr.es/keel/

KEEL: Knowledge Extraction based on Evolutionary Learning 2008 August

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The aim of this project is to develop a Computational Environment for integrating the design and use of knowledge extraction models from data using evolutionary algorithms. Genetic learning may also

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