Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

 

A Calculation Method for Dependency Degree of Condition Attribute Set using Discernibility Matrix

Volume 14, Number 3, March 2018, pp. 573-578
DOI: 10.23940/ijpe.18.03.p18.573578

Hongchan Lia, Junxing Liub, and Haodong Zhua

aSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, 450002, China
bNo.1 Middle School of ZhengZhou, Zhengzhou, Henan, 450007, China

(Submitted on July 17, 2017; First revised on October 27, 2017; Second revised on November 21, 2017; Accepted on December 21, 2017)


Abstract:

In the process of attribute reduction, the importance degree of a condition attribute is generally measured by means of the dependence degree between the condition attribute and the decision attribute set. If the dependence degree of the condition attribute is 0, we generally think that the condition attribute does not affect the decision results of the decision table and can be directly deleted form the condition attribute set. However, to some extent, it cuts off the connection of the condition attribute and other attributes, resulting in a great loss of valuable information in the decision table. Therefore, based on the fact that the dependency degree of the condition attribute set is more credible than the dependency degree of a single condition attribute, this paper researches the dependency degree of the condition attribute set and puts forward a calculation method for dependency degree of condition attribute set using a discernibility matrix. This paper also presents and proves a theorem to improve the proposed method. The proposed method can quickly get the discernibility matrix and can directly calculate the dependency degree of the condition attribute set. The theoretical analysis and the simulation experiment comparison results all show that the proposed method has better effectiveness and lower time complexity.

 

References: 20

  1. H. Dan, X. C. Yu, "Statistical Inference of Rough Set Dependence and Importance Analysis," IEEE Transactions on Fuzzy Systems, vol. 21, no. 6, pp. 1070-1079, 2013.
  2. J. Fan, Y. L. Jiang, Y. Liu, "Quick attribute reduction with generalized indiscernibility models," Information Sciences, vol. 397, pp. 15-36, 2017.
  3. S. T. Hu, Y. Q. He, "Rough decision theory and application," Beihang University Press, Beijing, 2006.
  4. D. Hu, X. C. Yu, J.Y. Wang, "Statistical Inference in Rough Set Theory Based on Kolmogorov–Smirnov Goodness-of-Fit Test," IEEE Transactions on Fuzzy Systems, vol. 25, no. 4, pp. 799-812, 2017.
  5. Y. Y. Huang, T. R. Li, C. Luo, et al, "Dynamic variable precision rough set approach for probabilistic set-valued information systems," Knowledge-Based Systems, vol. 122, pp. 131-147, 2017.
  6. U. Jamal, G. Rozaida, M. M. Deris, "An Empirical Analysis of Rough Set Categorical Clustering Techniques," Plos One, vol. 12, no. 1, pp. 1-22, 2017.
  7. A. Joshuva, V. Sugumaran , "Classification of Various Wind Turbine Blade Faults through Vibration Signals Using Hyperpipes and Voting Feature Intervals Algorithm," International Journal of Performability Engineering, vol. 13, no. 3, pp. 247-258, 2017.
  8. J. Konecny, "On attribute reduction in concept lattices: Methods based on discernibility matrix are outperformed by basic clarification and reduction," Information sciences, vol.415, pp.199-212, 2017.
  9. J. Y. Liang, F. Wang, C. Y. Dang, et al "An efficient rough feature selection algorithm with a multi-granulation view," International Journal of Approximate Reasoning, vol. 53, pp.912-926, 2012.
  10. L. Liu, B. S. Wang, Q. X. Zhong, et al, "A New Method for Decision Tree Based Discernibility Matrix and Degree of Consistent Dependence," Applied Mechanics and Materials, vol.743, pp. 390-394, 2015.
  11. W.M. Ma, B. Z. Sun, "Probabilistic rough set over two universes and rough entropy," International Journal of Approximate Reasoning, vol. 53, no. 4, pp. 608-619, 2012.
  12. C. U. Mba, H. A. Gabbar, S. Marchesiello, et al, "Fault Diagnosis in Flywheels: Case Study of a Reaction Wheel Dynamic System with Bearing Imperfections," International Journal of Performability Engineering, vol. 13, no. 4, pp. 362-373, 2017.
  13. I. K. Park, G. S. Choi, "Rough set approach for clustering categorical data using information-theoretic dependency measure," Information Systems, vol. 48, pp. 289-295, 2015.
  14. J. Qian, C. Y. Dang, X. D. Yue, et al, "Attribute reduction for sequential three-way decisions under dynamic granulation," International Journal of Approximate Reasoning, vol. 85, pp. 196-216, 2017.
  15. M. Salamó, M. López-Sánchez, "Rough set based approaches to feature selection for case-based reasoning classifiers," Pattern Recognition Letters, vol. 32, no. 2, pp.280-292, 2011.
  16. A. Sanchis, M. J. Segovia, J. A. Gil, et al, "Rough Sets and the role of the monetary policy in financial stability (macroeconomic problem) and the prediction of insolvency in insurance sector (microeconomic problem)," European Journal of Operational Research, vol. 181, no. 3, pp. 1554-1573, 2007.
  17. Y. H. She, X. L. He, H. X. Shi, et al, "A multiple-valued logic approach for multigranulation rough set model, " International Journal of Approximate Reasoning, vol. 82, no. 1, pp. 270-284, 2017.
  18. B. Yang, B. Q. Hu, "On some types of fuzzy covering-based rough sets,” Fuzzy Sets and Systems, vol. 312, pp. 36-65, 2017.
  19. X. X. Zhang, D. G. Chen, E. C. C. Tsang, "Generalized dominance rough set models for the dominance intuitionistic fuzzy information systems," Information Sciences, vol. 378, pp. 1-25, 2017.
  20. X. Y. Zhang, D. Q. Miao, "Three-way attribute reducts," International Journal of Approximate Reasoning, vol. 88, pp.401-434, 2017.

 

Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

Attachments:
Download this file (IJPE-2018-03-18.pdf)IJPE-2018-03-18.pdf[A Calculation Method for Dependency Degree of Condition Attribute Set using Discernibility Matrix]230 Kb
 

CURRENT ISSUE

Prev Next

Temporal Multiscale Consumption Strategies of Intermittent Energy based on Parallel Computing

Huifen Chen, Yiming Zhang, Feng Yao, Zhice Yang, Fang Liu, Yi Liu, Zhiheng Li, and Jinggang Wang

Read more

Decision Tree Incremental Learning Algorithm Oriented Intelligence Data

Hongbin Wang, Ci Chu, Xiaodong Xie, Nianbin Wang, and Jing Sun

Read more

Spark-based Ensemble Learning for Imbalanced Data Classification

Jiaman Ding, Sichen Wang, Lianyin Jia, Jinguo You, and Ying Jiang

Read more

Classification Decision based on a Hybrid Method of Weighted kNN and Hyper-Sphere SVM

Peng Chen, Guoyou Shi, Shuang Liu, Yuanqiang Zhang, and Denis Špelič

Read more

An Improved Algorithm based on Time Domain Network Evolution

Guanghui Yan, Qingqing Ma, Yafei Wang, Yu Wu, and Dan Jin

Read more

Auto-Tuning for Solving Multi-Conditional MAD Model

Feng Yao, Yi Liu, Huifen Chen, Chen Li, Zhonghua Lu, Jinggang Wang, Zhiheng Li, and Ningming Nie

Read more

Smart Mine Construction based on Knowledge Engineering and Internet of Things

Xiaosan Ge, Shuai Su, Haiyang Yu, Gang Chen, and Xiaoping Lu

Read more

A Mining Model of Network Log Data based on Hadoop

Yun Wu, Xin Ma, Guangqian Kong, Bin Wang, and Xinwei Niu

Read more
This site uses encryption for transmitting your passwords. ratmilwebsolutions.com