Label Selection Algorithm Based on Ant Colony Optimization and Reinforcement Learning for Multi-label Classification

Multi-label classification handles scenarios where an instance can be annotated with multiple non-exclusive but semantically related labels simultaneously. Despite significant progress, multi-label classification is still challenging due to the emergence of multiple applications leading to high-dimensional label spaces. Researchers have generalized feature dimensionality reduction techniques to label space by using label correlation information, and obtained two techniques: label embedding and label selection. There have been many successful algorithms in label embedding, but less attention has been paid to label selection. In this paper, we propose a label selection algorithm for multi-label classification: LS-AntRL, which combines ant colony optimization (ACO) and reinforcement learning (RL). This method helps ant colony algorithms search better in the search space by using temporal difference (TD) RL algorithm to learn directly from the experience of ants. For heuristic learning, we need to model the ACO problem as a RL problem, that is, to model label selection as a Markov decision process (MDP), where the label represents the state, and each ant selecting unvisited labels represents a set of actions. The state transition rules of the ACO algorithm constitute the transition function in the MDP, and the state value function is updated by TD formula to form a heuristic function in ACO. After performing label selection, we train a binary weighted neural network to recover low-dimensional label space back to the original label space. We apply the above model to five benchmark datasets with more than 100 labels. Experimental results show that our method achieves better classification performance than other advanced methods in terms of two performance evaluation metrics (Precision@n and DCG@n).