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基于Fuzzy Rough Set约简的健康快速评估算法
Health Rapid Assessment Based on Fuzzy Rough Set Reduction Algorithm

DOI: 10.12677/HJDM.2015.52005, PP. 32-38

Keywords: Rough Set,模式识别,属性约简,Fuzzy 理论
Rough Set
, Pattern Recognition, Attribute Reduction, Fuzzy Theory

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Abstract:

健康是人们生活水平优良的重要指标,但是如何快速基于生理检测指标来评估其健康水平是智慧医学所关注的核心问题。由于各种生理检测指标之间具有冗余特性,因此对所分析的生理检测指标进行属性分析具有重要作用。Rough Set在知识挖掘方面具有很强的处理能力,本文依据Rough Set对Fuzzy数据的处理方法,提出一种Fuzzy Rough Set约简算法,该算法可以实现利用生理检测数据对健康快速评估。同时由于使用来Fuzzy理论,所以其结果更加适合人们的接受方式,更能体现数据本质的概率特性。对实际数据的仿真分析可以发现该算法在健康快速评估上具有高的识别准确度。
Health is a good important indicator of people’s living standard, but how to quickly evaluate health level based on physiological index is the core issue of concern for medical wisdom. Due to redundancy characteristics between various physiological indicators to determine, it’s important to analyze the properties for physical test indexes. Rough Set has a strong processing capacity, in terms of knowledge discovery. This paper based on Rough Set of Fuzzy data processing method, puts forward a kind of Fuzzy Rough Set reduction algorithm; this algorithm can implement rapid assessment to the health using physical testing data. At the same time due to the use of Fuzzy theory, the result is more suitable for people to accept, and can reflect the probability characteris-tics in nature of the data. The simulation analysis of actual data can be found that the algorithm has a high recognition accuracy in the rapid assessment of health.

References

[1]  朱金伟, 鞠时光 (2006) 基于数据挖掘的中医药数据预处理方法. 计算机工程, 15, 280-283.
[2]  杨淑莹 (2011) 模式识别与智能计算. 电子工业出版社, 北京, 1-193.
[3]  Jensen, R. and Shen, Q. (2009) Are more features better? A response to attributes reduction using fuzzy rough sets. IEEE Transactions on Fuzzy Systems, 17, 1456-1458
[4]  苗夺谦, 李道国 (2008) 粗糙集理论、算法与应用. 清华大学出版社, 北京, 24-230.
[5]  Chen, D., Zhang, L., Zhao, S.Y., Hu, Q.H. and Zhu, P.F. (2012) A novel algorithm for finding reducts with fuzzy rough sets. IEEE Transactions on Fuzzy Systems, 20, 385-389.
[6]  Zhao, S.Y., Tsang, E.C.C., Chen, D.G. and Wang, X.Z. (2010) Building a rule-based classifier—A fuzzy-rough set approach. IEEE Transactions on Knowledge and Data Engineering, 22, 624-638.
[7]  Huang, H.H. and Kuo, Y.H. (2010) Cross-lingual document representation and semantic similarity measure: A fuzzy set and rough set based approach. IEEE Transactions on Fuzzy Systems, 18, 1098-1111.
[8]  Cock, M.D., Cornelis, C. and Kerre, E.E. (2007) Fuzzy rough sets: The forgotten step. IEEE Transactions on Fuzzy Systems, 15, 121-130.
[9]  Starczewski, J.T. (2010) General type-2 FLS with uncertainty generated by fuzzy rough sets. FUZZ Conference Proceeding, 1-6.
[10]  Hu, Q.H., Yu, D.R., Pedrycz, W. and Chen, D.G. (2011) Kernelized fuzzy rough sets and their applications. IEEE Transactions on Knowledge and Data Engineering, 23, 1649-1667.

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