Featureselection In High-Dimensional Datasetsusing Cuckoo Optimization Algorithm
Feature selection is a process commonly used in machine learning. Based on Binary Cuckoo Optimization
Algorithm (BCOA) and information theory, this paper proposes a new filter feature selection method for classification
problems. The proposed algorithm is based on BCOA and the Mutual Information (MI) of eachpair of features, which
determines the relevance and redundancy of the selected feature subset. Different weights for the relevance and redundancy
in the fitness functions of the proposed algorithm are used to further improve their performance in terms of the number of
features and the classification accuracy. In the experiments, an Artificial Neural Network (ANN) is employed to evaluate the
classification accuracy of the selected feature subset on the test sets of six datasets. The results show that proposed
algorithms can significantly reduce the number of features and achieve high classification accuracy in almost all cases.
Keywords- Feature Selection, Mutual Information, Binary Cuckoo Optimization, Filters.