Abstract
At present, the time complexity of multi-label algorithm based on second-order and higher-order strategies is usually increased due to the increase of the size of the label set. To solve this problem, this chapter proposes a Multi-label algorithm by mining local label correlations (MLMC) with efficiency as the optimization objective. Firstly, from the broad dimension of the correlation between labels, the correlation between labels is used to optimize the classification model. Finally, the nearest neighbor rough set theory is used to consider the positive and negative dependencies between the labels. Experiments show that the MLMC has better accuracy and efficiency than the classical methods.
S. Chong—Major Projects of Technological Innovation in Hubei Province (No. 2019ABA101).
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This work is supported and assisted by the Research Team of Key Technologies of Smart Agriculture and Intelligent Information Processing and Optimization (No. 2019ABA101).
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Sun, C., He, K., Zhou, W., Song, Z., Tie, J. (2022). Multi-label Algorithm Based on the Second and Higher Order Strategies. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 854. Springer, Singapore. https://doi.org/10.1007/978-981-16-9423-3_54
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