An unsupervised feature selection algorithm based on ant colony optimization

• Our proposed feature selection method is classified as unsupervised, filter and multivariate.
• The possible dependencies between features are considered to reduce the redundancy among the selected features.
• The proposed method manages the trade-off between computational time and quality of the results.
• The method has been compared to well-known univariate and multivariate methods on the different classifiers.
• The experimental results indicate that the method outperforms the unsupervised methods and is comparable with the supervised methods.

Feature selection is a combinatorial optimization problem that selects most relevant features from an original feature set to increase the performance of classification or clustering algorithms. Most feature selection methods are supervised methods and use the class labels as a guide. On the other hand, unsupervised feature selection is a more difficult problem due to the unavailability of class labels. In this paper, we present an unsupervised feature selection method based on ant colony optimization, called UFSACO. The method seeks to find the optimal feature subset through several iterations without using any learning algorithms. Moreover, the feature relevance will be computed based on the similarity between features, which leads to the minimization of the redundancy. Therefore, it can be classified as a filter-based multivariate method. The proposed method has a low computational complexity, thus it can be applied for high dimensional datasets. We compare the performance of UFSACO to 11 well-known univariate and multivariate feature selection methods using different classifiers (support vector machine, decision tree, and naïve Bayes). The experimental results on several frequently used datasets show the efficiency and effectiveness of the UFSACO method as well as improvements over previous related methods.