A Hybrid Feature Selection Approach for Data Clustering Based on Ant Colony Optimization

Abstract

Machine learning, data mining, and pattern recognition all require feature selection when working with high-dimensional data. Feature selection helps in improving the prediction accuracy and significantly reduces the computation time. The problem is that many of the feature selection algorithms use a sequential search strategy to choose the most important features. This means that each time you add or remove a feature from the dataset, you get stuck in a local optimum. This paper proposes a hybrid feature selection technique based on ant colony optimization that randomly selects features and quantifies their quality using K-means clustering in terms of silhouette index and laplacian score. The proposed hybrid feature selection technique allows for random selection of features, which facilitates a better exploration of feature space and avoids the problem of being trapped in a local optimal solution, while also generating a global optimal solution. Furthermore experimental investigation shows that the proposed method outperforms the state-of-the-art method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.