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Related families-based methods for updating reducts under dynamic object sets

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Abstract

Due to rapid growth of data with respect to time, feature selection in dynamic covering decision information systems (DCDISs) is an important research direction of covering rough set theory, and we have not observed researches on related families-based methods for updating reducts of DCDISs with dynamic object variations. In this paper, we first introduce the concepts of covering decision approximation spaces (CDASs) and dynamic covering decision approximation spaces (DCDASs) when varying object sets, and illustrate the relationship between related sets of CDASs and those of DCDASs. Then incremental learning methods based on related families are provided for feature selection in DCDISs with the addition and deletion of objects. Finally, we develop the corresponding heuristic incremental algorithms for feature selection in DCDISs and employ experimental results on benchmark datasets to demonstrate that these algorithms give satisfactory results in terms of running times.

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Acknowledgements

We are very grateful to the anonymous reviewers for their valuable suggestions. This work is supported by the National Natural Science Foundation of China (Nos. 61603063, 11771059, 61573255, 61673301), the Natural Science Foundation of Hunan Province (Nos. 2018JJ3518, 2018JJ2027), and the Scientific Research Fund of Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering (No. 2018MMAEZD10).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, 410114, Hunan, People’s Republic of China

    Guangming Lang

  2. Department of Computer Science and Technology, Tongji University, Shanghai, 201804, People’s Republic of China

    Guangming Lang & Hongyun Zhang

  3. Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering, Changsha, 410114, Hunan, People’s Republic of China

    Guangming Lang

  4. College of Mathematics and Econometrics, Hunan University, Changsha, 410004, Hunan, People’s Republic of China

    Qingguo Li & Mingjie Cai

  5. Faculty of Software and Information Science, Iwate Prefectural University, Iwate, 020-0693, Japan

    Hamido Fujita

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  1. Guangming Lang
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  2. Qingguo Li
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  3. Mingjie Cai
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  4. Hamido Fujita
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  5. Hongyun Zhang
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Correspondence to Mingjie Cai.

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Lang, G., Li, Q., Cai, M. et al. Related families-based methods for updating reducts under dynamic object sets. Knowl Inf Syst 60, 1081–1104 (2019). https://doi.org/10.1007/s10115-019-01359-w

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  • DOI: https://doi.org/10.1007/s10115-019-01359-w

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