Abstract
Multi-label classification deals with a special supervised classification problem where any instance could be associated with multiple class labels simultaneously. As various applications emerge continuously in big data field, their feature dimensionality also increases correspondingly, which generally increases computational burdens and even deteriorates classification performance. To this end, feature selection has become a necessary pre-processing step, in which it is still challenging to design an effective feature selection criterion and its corresponding optimization strategy. In this paper, a novel feature selection criterion is constructed via maximizing label correlation-aware relevance between features and labels, and minimizing redundance among features. Then this criterion is optimized using binary particle swarm optimization with mutation operation, to search for a globally optimal feature selection solution. The experiments on four benchmark data sets illustrate that our proposed feature selection algorithm is superior to three state-of-the-art methods according to accuracy and F1 performance evaluation metrics.
Supported by Natural Science Foundation of China (NSFC) under Grants 62076134 and 61703096.
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Zhu, X., Tao, Y., Li, J., Xu, J. (2021). Multi-label Feature Selection Algorithm via Maximizing Label Correlation-Aware Relevance and Minimizing Redundance with Mutation Binary Particle Swarm Optimization. In: Golfarelli, M., Wrembel, R., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2021. Lecture Notes in Computer Science(), vol 12925. Springer, Cham. https://doi.org/10.1007/978-3-030-86534-4_25
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