TOPSIS-ACO based feature selection for multi-label classification

This paper introduces an approach for optimal feature selection (FS) in multi-label (ML) data, where each sample can be associated with multiple class labels. The complexity of the feature space is significantly increased in comparison to single-label data, making decision-making challenging. Therefore, FS plays a crucial role in ML classification. The proposed method, called ML-TOPSIS-ACO, leverages the ‘Technique of Order Preference by Similarity to Ideal Solution’ (TOPSIS) as a Multi-Criteria Decision Making (MCDM) technique and ‘Ant Colony Optimization’ (ACO) as a meta-heuristic technique. The ML-TOPSIS-ACO method operates in two phases. In the first phase, Modified TOPSIS (MTOPSIS) is used to perform feature ranking and select the most important features. These selected features are then passed to Modified-ACO for feature re-ranking, aiming to identify the best subset of features for ML classification. To evaluate the effectiveness of ML-TOPSIS-ACO, experiments were conducted on nine benchmark datasets. The experimental results demonstrate that ML-TOPSIS-ACO outperforms other existing methods, achieving a winning percentage of 83%.

本文提出了一种面向多标签（Multi-label，ML）数据的最优特征选择（Feature Selection，FS）方法，此类数据中的每个样本可对应多个类别标签。相较于单标签数据，多标签数据的特征空间复杂度显著提升，使得决策过程更具挑战性，因此特征选择在多标签分类任务中扮演着至关重要的角色。本文所提出的方法命名为ML-TOPSIS-ACO，该方法将「逼近理想解排序法（Technique of Order Preference by Similarity to Ideal Solution，TOPSIS）」作为多准则决策（Multi-Criteria Decision Making，MCDM）技术，同时将「蚁群优化（Ant Colony Optimization，ACO）」作为元启发式技术加以利用。ML-TOPSIS-ACO方法分为两个阶段执行：第一阶段采用改进逼近理想解排序法（Modified TOPSIS，MTOPSIS）完成特征排序，并筛选出最为重要的特征；随后将筛选得到的特征输入至改进蚁群优化（Modified-ACO）中进行特征重排序，旨在识别出适用于多标签分类任务的最优特征子集。为验证ML-TOPSIS-ACO的有效性，研究团队在9个基准数据集上开展了实验。实验结果表明，ML-TOPSIS-ACO的性能优于其他现有方法，胜出率达到83%。