Multi-Label Feature Selection with Graph-based Ant Colony Optimization and Generalized Jaccard Similarity

Abstract

Multi-label learning is a technique that assigns multiple class labels to each data instance. The growth of digital technology resulted in the development of high-dimensional applications in real-world scenarios. Feature selection approaches are extensively used to reduce dimensionality in multi-label learning. The main problems of the recommender system are determining the best match of futures among users but have not engaged with previously. This paper proposes a strategy for selecting features using ant colony optimization (ACO) that incorporates mutual knowledge. The proposed method utilizes ACO to rank features based on their significance. Thus, the search space is mapped to a graph, and each ant traverses the graph, selecting a predetermined number of features. A new information-theoretical metric is introduced to evaluate the features chosen by each ant. Jaccard generalized similarity coefficient is used to select the most suitable communication target for efficient learning outcomes. Mutual information is employed to assess each features relevance to a set of labels and identify redundant features. Pheromones are assigned values based on the effectiveness of the ants in solving the problem. Finally, the features are ranked based on their pheromone values, and the top-ranked features are selected as the final set of attributes. The proposed method is evaluated using real-world datasets. The findings demonstrate that the proposed method outperforms most of existing and advanced approaches. This paper presents a novel feature selection approach for multi-label learning based on ACO. The experimental results confirm the effectiveness of the proposed method compared to existing techniques.

Keywords

• Multi-label optimization
• Feature selection
• Ant Colony
• Relevance-redundancy
• Generalized Jaccard similarity