Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

@article{Franco2014FeatureSB,
  title={Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps},
  author={Emiro de la Hoz Franco and Eduardo de la Hoz Correa and Andr{\'e}s Ortiz and Julio Ortega and Antonio Mart{\'i}nez-{\'A}lvarez},
  journal={Knowl.-Based Syst.},
  year={2014},
  volume={71},
  pages={322-338}
}
Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by… CONTINUE READING
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