Improving a Fuzzy Discretization Process by Bagging

José Manuel Cadenas, María del Carmen Garrido, Raquel Martínez


Classification problems in which the number of attributes is larger than the number of examples are increasingly common with rapid technological advances in data collection. Also numerical data are predominant in real world applications and many algorithms in supervised learning are restricted to discrete attributes. Focusing on these issues, we proposed an improvement in a fuzzy discretization method by means of introduction of a bagging process in the different phases of the method. The bagging process tries to solve problems which can appear with small size datasets. Also we show the benefits that bagging introduces in the method by means of several experiments. The experiments are validated by means of statistical test.


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Paper Citation

in Harvard Style

Manuel Cadenas J., del Carmen Garrido M. and Martínez R. (2013). Improving a Fuzzy Discretization Process by Bagging . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 201-212. DOI: 10.5220/0004553402010212

in Bibtex Style

author={José Manuel Cadenas and María del Carmen Garrido and Raquel Martínez},
title={Improving a Fuzzy Discretization Process by Bagging},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013)},

in EndNote Style

JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013)
TI - Improving a Fuzzy Discretization Process by Bagging
SN - 978-989-8565-77-8
AU - Manuel Cadenas J.
AU - del Carmen Garrido M.
AU - Martínez R.
PY - 2013
SP - 201
EP - 212
DO - 10.5220/0004553402010212