Co-operative Co-evolutionary Many-objective Embedded Multi-label Feature Selection with Decomposition-based PSO

Multi-label classification is an emerging machine-learning problem involving the prediction of a set of class labels based on the instance's features. In real-world problems, there are hundreds or thousands of features, many of which are irrelevant or redundant, resulting in a large search space for feature selection, i.e. to find small and discriminate feature subsets that improve the classification performance. Feature selection for multi-label classification is a many-objective optimisation problem with more than three main conflicting objectives: one of which is to reduce the number of selected features. There are many metrics for measuring multi-label classification performance, each of which can conflict with one another depending on the task. Hence, multi-label feature selection is a many-objective optimisation problem when three or more classification metrics and the number of selected features are optimised. In this paper, we propose to combine multi-label feature selection with evolutionary many-objective optimisation to address the above challenges and handle the trade-offs between multiple classification metrics and the number of selected features, using a decomposition-based algorithm. The results demonstrate that our proposed method is capable of finding discriminative and small feature subsets that can significantly improve the classification performances in comparison with other many-objective feature selection approaches.