Dimension Reduction with Coevolutionary Genetic Algorithm for Text Classification

Tatiana Gasanova, Roman Sergienko, Eugene Semenkin, Wolfgang Minker


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.


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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

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,},

in EndNote Style

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