|
|
基于链分解的多标签分类属性约简
|
Abstract:
本文提出了基于链分解的多标签属性约简方法。通过考虑标签之间的相关性，将标签进行排序，根据排序方法，多标签问题被分解成单标签链的形式，对于链中每一个子问题通过粗糙集方法重新定义下近似、正域、依赖度，并进行属性约简。实验结果表明，该方法能在不降低分类精度的情况下去除大部分冗余属性。
In this paper, a new multi-label attribute reduction algorithm based on the chain decomposition is proposed. Considering the correlation between the labels, the labels are sorted. According to the sorting method, the multi-label problem is decomposed into a single-label problem chain. For each sub-problem, the lower approximation, the positive region and the dependency are redefined by the rough set method, and the attributes are reduced. Experimental results show that the algo-rithm can remove most of the redundant attributes without reducing the classification accuracy.
| [1] | Read, J., Pfahringer, B., Holmes, G. and Frank, E. (2009) Classifier Chains for Multi-Label Classification. In: Buntine, W., Grobelnik, M. and Shawe-Taylor, J., Eds., Lecture Notes in Artificial Intelligence 5782, Springer, Berlin, 254-269.
https://doi.org/10.1007/978-3-642-04174-7_17 |
| [2] | Read, J., Pfahringer, B., Holmes, G. and Frank, E. (2011) Classifier Chains for Multi-Label Classification. Machine. Learning, 85, 333-359. https://doi.org/10.1007/s10994-011-5256-5 |
| [3] | Pawlak, Z. (2011) Rough Sets. International Journal of Computer and Information Sciences, 11, 34l-356.
https://doi.org/10.1007/BF01001956 |
| [4] | Hu, Q.H., Yu, D.R., Liu, J.F. and Wu, C. (2008) Neighborhood Rough Set Based Heterogeneous Feature Subset Selection. Information Sciences, 178, 3577-3594. https://doi.org/10.1016/j.ins.2008.05.024 |
| [5] | Li, H., Li, D., Zhai, Y., Wang, S. and Zhang, J. (2016) A Novel At-tribute Reduction Approach for Multi-Label Data Based on Rough Set Theory. Information Sciences, 367-368, 827-847. https://doi.org/10.1016/j.ins.2016.07.008 |
| [6] | Lin, Y., Li, Y., Wang, C. and Chen, J. (2018) Attribute Reduction for Multi-Label Learning with Fuzzy Rough Set. Knowledge-Based Systems, 152, 51-61. https://doi.org/10.1016/j.knosys.2018.04.004 |
| [7] | Liu, J., Lin, Y., Li, Y., Weng, W. and Wu, S. (2018) Online Multi-Label Streaming Feature Selection Based on Neighborhood Rough Set. Pattern Recognition, 84, 273-287. https://doi.org/10.1016/j.patcog.2018.07.021 |
| [8] | Lin, Y., Hua, Q., Liu, J., Chen, J. and Duan, J. (2016) Mul-ti-Label Feature Selection Based on Neighborhood Mutual Information. Applied Soft Computing, 38, 244-256. https://doi.org/10.1016/j.asoc.2015.10.009 |
| [9] | Lin, Y., Li, Y., Wang, C. and Chen, J. (2018) Attribute Reduction for Multi-Label Learning with Fuzzy Rough Set. Knowledge-Based Systems, 152, 51-61. https://doi.org/10.1016/j.knosys.2018.04.004 |
| [10] | Fan, X., Chen, Q., Qiao, Z., Wang, C. and Ten, M. (2020) At-tribute Reduction for Multi-Label Classification Based on Labels of Positive Region. Soft Computing, 24, 14039-14049. https://doi.org/10.1007/s00500-020-04780-4 |
