Dimension Reduction with Coevolutionary Genetic Algorithm for Text Classification

Tatiana Gasanova, Roman Sergienko, Eugene Semenkin, Wolfgang Minker

2014

Abstract

Text classification of large-size corpora is time-consuming for implementation of classification algorithms. For this reason, it is important to reduce dimension of text classification problems. We propose a method for dimension reduction based on hierarchical agglomerative clustering of terms and cluster weight optimization using cooperative coevolutionary genetic algorithm. The method was applied on 5 different corpora using several classification methods with different text preprocessing. The method reduces dimension of text classification problem significantly. Classification efficiency increases or decreases non-significantly after clustering with optimization of cluster weights.

Paper Citation

in Harvard Style

Gasanova T., Sergienko R., Semenkin E. and Minker W. (2014). Dimension Reduction with Coevolutionary Genetic Algorithm for Text Classification . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-039-0, pages 215-222. DOI: 10.5220/0005020702150222

in Bibtex Style

@conference{icinco14,
author={Tatiana Gasanova and Roman Sergienko and Eugene Semenkin and Wolfgang Minker},
title={Dimension Reduction with Coevolutionary Genetic Algorithm for Text Classification},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2014},
pages={215-222},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005020702150222},
isbn={978-989-758-039-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Dimension Reduction with Coevolutionary Genetic Algorithm for Text Classification
SN - 978-989-758-039-0
AU - Gasanova T.
AU - Sergienko R.
AU - Semenkin E.
AU - Minker W.
PY - 2014
SP - 215
EP - 222
DO - 10.5220/0005020702150222