A Hybrid Approach for Feature Subset Selection using Ant Colony Optimization and Multi-Classifier Ensemble

An active area of research in data mining and machine learning is dimensionality reduction. Feature subset selection is an effective technique for dimensionality reduction and an essential step in successful data mining applications. It reduces the number of features, removes irrelevant, redundant, or noisy features, and enhances the predictive capability of the classifier. It provides fast and cost-effective predictors and leading to better model comprehensibility. In this paper, we proposed a hybrid approach for feature subset selection. It is a filter based method in which a classifier ensemble is coupled with Ant colony optimization algorithm to enhance the predictive accuracy of filters. Extensive experimentation has been carried out on eleven data sets over four different classifiers. All of the data sets are available publically. We have compared our proposed method with numerous filter and wrapper based methods. Experimental results indicate that our method has remarkable ability to generate subsets with reduced number of features. Along with it, our proposed method attained higher classification accuracy.

@article{Naseer2018,
  title     = {A Hybrid Approach for Feature Subset Selection using Ant Colony Optimization and Multi-Classifier Ensemble},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {1},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Anam Naseer and Waseem Shahzad and Arslan Ellahi},
  doi       = {10.14569/IJACSA.2018.090142},
  url       = {https://doi.org/10.14569/IJACSA.2018.090142}
}