article · 2007
Data Preprocessing in Knowledge Discovery with Fuzzy-Evolutionary Algorithms
Antonio Gomez-Skarmeta, Fernando Jiménez, Jesús Ibáñez, Antonio F. G'omez-skarmeta Fern
Cited by 0Abstract and details only
Abstract
Databases in the real world are often dynamic, incomplete, noisy, imprecise and very large and most of the problems which appear in the data mining step within the knowledge discovery process are due to an inadequate preprocessing of the data present in these databases. The use of techniques that can cope with the imprecision present in the data, represent a new approach which has been successful in dealing with problems of imprecise and uncertain knowledge in the context of complex systems. In this paper an hybrid learning systems, that combines fuzzy-evolutionary techniques in order to find useful features or variables to represent the data as a preprocessing step, is being investigated. Keywords: Data preprocessing, knowledge discovery, data mining, evolutionary algorithms, fuzzy sets. 1 Introduction Given a set of data for which we presume some functional dependency, the question arises whether there is a suitable methodology to derive (fuzzy) rules from these data that character...
Cite this paper
Gomez-Skarmeta, A., Jiménez, F., Ibáñez, J., & Fern, A. F. G.-S. (2007). Data Preprocessing in Knowledge Discovery with Fuzzy-Evolutionary Algorithms.
Gomez-Skarmeta, Antonio, et al. “Data Preprocessing in Knowledge Discovery with Fuzzy-Evolutionary Algorithms.” 2007.
Gomez-Skarmeta, Antonio, Fernando Jiménez, Jesús Ibáñez, and Antonio F. G'omez-skarmeta Fern. 2007. “Data Preprocessing in Knowledge Discovery with Fuzzy-Evolutionary Algorithms.”
Gomez-Skarmeta, A. et al. (2007) ‘Data Preprocessing in Knowledge Discovery with Fuzzy-Evolutionary Algorithms’.
@article{gomezskarmeta2007data,
title = {{Data Preprocessing in Knowledge Discovery with Fuzzy-Evolutionary Algorithms}},
author = {Gomez-Skarmeta, Antonio and Jiménez, Fernando and Ibáñez, Jesús and Fern, Antonio F. G'omez-skarmeta},
year = {2007}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.