Multi-label classification is the process of specifying more than one class label for each instance. The high-dimensional data in various multi-label classification tasks have a direct impact on reducing the efficiency of traditional multi-label classifiers. To tackle this problem, feature selection is used as an effective approach to retain relevant features and eliminating redundant ones to reduce dimensionality. Multi-label classification has a wide range of real-world applications such as image classification, emotion analysis, text mining and bioinformatics. Moreover, in recent years researchers have focused on applying swarm intelligence methods in selecting prominent features of multi-label data. After reviewing various researches, it seems there are no researches that provide a review of swarm intelligence-based methods for multi-label feature selection. Thus, in this study, a comprehensive review of different swarm intelligence and evolutionary computing methods of feature selection presented for the tasks of multi-label classification. To this end, in this review, we have investigated most of the well-known and state-of-the-art methods and categorize them based on different perspectives. We then provided the main characteristics of the existing multi-label feature selection techniques and compared them analytically. We also introduce benchmarks, evaluation measures and standard datasets to facilitate research in this field. Moreover, we performed some experiments to compare existing works and at the end of this survey, some challenges, issues and open problems of this field are introduced to be considered by researchers in future.
Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: An application to proteomics data
