Abstract
Instances in multi-label data sets are generally described as a high-dimensional feature vector, as brings the “curse of dimensionality” problem. To ease this problem, some multi-label feature selection algorithms have been proposed. However, they all handle feature selection problems with the assumption that all candidate features are available beforehand. While in some real applications, feature selection must be conducted in the online manner with dynamic features, for example, novel topics arise constantly with a set of features in social networks. Online streaming feature selection (OSFS), dealing with dynamic features, has attracted intensive interest in recent years. Some online feature selection methods are designed for single-label applications, They can not be directly applied in multi-label scenarios. In this paper, we propose a multi-label online streaming feature selection algorithm based on spectral granulation and mutual information (ML-OSMI), which takes high-order label correlations into consideration. Moreover, comprehensive experiments are conducted to verify the effectiveness of the proposed algorithm on twelve multi-label high-dimensional benchmark data sets.
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Acknowledgements
This work was supported by the National Key Research and Development Program of China (Grant no. 2016YFB1000900), the National Natural Science Foundation of China (Grant nos. 61572091, 61772096), Chongqing Basic and Frontier Research Project (cstc2015jcyjA40018) and The Science and Technology Project Affiliated to the Education Department of Chongqing Municipality (KJ1500438).
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Wang, H., Yu, D., Li, Y., Li, Z., Wang, G. (2018). Multi-label Online Streaming Feature Selection Based on Spectral Granulation and Mutual Information. In: Nguyen, H., Ha, QT., Li, T., Przybyła-Kasperek, M. (eds) Rough Sets. IJCRS 2018. Lecture Notes in Computer Science(), vol 11103. Springer, Cham. https://doi.org/10.1007/978-3-319-99368-3_17
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