Abstract
Attribute reduction is viewed as one of the most important topics in rough set theory and there have been many researches on this issue. In the real world, partially labeled data is universal and cost sensitivity should be taken into account under some circumstances. However, very few studies on attribute reduction for partially labeled data with test cost have been carried out. In this paper, based on mutual information, the significance of an attribute in partially labeled decision system with test cost is defined, and for labeled data, a heuristic attribute reduction algorithm TCSPR is proposed. Experimental results show the impact of test cost on reducts for partially labeled data and comparative experiments of classification accuracy indicate the effectiveness of the proposed method.
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Acknowledgements
The authors would like to thank the anonymous reviewers for their constructive comments that help improve the manuscript. This research was supported by the National Key R&D Program of China (213), National Natural Science Foundation of China (61673301), Major Project of Ministry of Public Security (20170004), and the Open Research Funds of State Key Laboratory for Novel Software Technology (KFKT2017B22).
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Hu, S., Miao, D., Zhang, Z., Luo, S., Zhang, Y., Hu, G. (2018). A Test Cost Sensitive Heuristic Attribute Reduction Algorithm for Partially Labeled Data. 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_20
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