Neural Information Processing
Correlation based feature selection method
International Journal of Bio-Inspired Computation
A filter based feature selection approach using lempel ziv complexity
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Computer Methods and Programs in Biomedicine
A novel divide-and-merge classification for high dimensional datasets
Computational Biology and Chemistry
Feature subset selection using binary gravitational search algorithm for intrusion detection system
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
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One of the problems that have to be overcome in classification tasks is high data dimensionality. Therefore, dimensionality reduction techniques such as feature selection have to be employed. Feature selection involves univariate or multivariate evaluation of features with respect to the classification accuracy. Pairwise feature selection was recently proposed as a trade-off between selection process complexity and the need to analyze relationships between features. In our previous work we have proposed a correlation-based modification of the pairwise feature selection. In this paper we present the results of the experiments in which we have compared the correlation-based feature selection strategy with the unmodified pairwise approach. The experiments were performed using neural network classifiers on commonly used benchmark data sets.