Volume 14, Issue 3 (12-2017)                   JSDP 2017, 14(3): 51-64 | Back to browse issues page

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mirzaee E, esmaeilpour M. A New Hybrid Method to Increase the Prediction in Data Reduced Using Rough Set and Swarm Intelligence Model. JSDP 2017; 14 (3) :51-64
URL: http://jsdp.rcisp.ac.ir/article-1-461-en.html
A New Hybrid Method to Increase the Prediction in Data Reduced Using Rough Set and Swarm Intelligence Model
Elahe Mirzaee , Mansour Esmaeilpour *
Islamic Azad University, Hamedan Branch
Abstract:   (4926 Views)

Designing a system with an emphasis on minimal human intervention helps users to explore information quickly. Adverting to methods of analyzing large data is compulsory as well. Hence, utilizing power of the data mining process to identify patterns and models become more essential from aspect of relationship between the various elements in the database and discover hidden knowledge. Therefore, Rough set theory can be used as a tool to explore data dependencies and reducing features outlined in a data set. The main purpose of the rough theory is to obtain approximate concepts of acquired data. This theory is a powerful mathematical tool for arguing in ambiguous and indeterminate terms that provides methods for remove and reduce unrelated or excessive knowledge information on the data sets. This process of data reduction is based on the main task of the system, and without losing the basic data of the data sets. Rough set theory can play a very effective role to support decision-making systems, but in some cases, with increasing data volumes, there are inconsistent or collisional results which using swarm intelligence-based methods can choose the best of the contradictory, effectless or dummy data. This will bring interesting, unexpected and valuable structures from within a wide range of data. Since the ant colony optimization compares all the exploratory paths generated by each ant and the best route is selected from the existing paths, so considering the improvement of the selecting the main features and improving the theory of the Rough set, paths are not eliminated from the possible paths. In this research, the combination of the ant colony optimization and rough set theory have been used to find the subset of the main features and to delete the inappropriate information with the loss of the minimum information. This research will improve the features reduction technique employment Rough set theory and ant colony optimization. The gist of this research is removing useless information with minimal information loss. The results on petroleum prices data evaluation demonstrate that the hybrid method is more efficient than recent methods.
 

Keywords: Rough Set Theory, Swarm Intelligence, Ant Colony Optimization, Feature Reduction
Full-Text [PDF 3773 kb]   (2665 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2015/11/21 | Accepted: 2016/10/29 | Published: 2018/01/29 | ePublished: 2018/01/29
References
1. [1] J. Wang, "Reduction Algorithms Based on Discernibility Matrix: The Ordered Attributes Method," Journal of Computer Science & Technology, Vol. 16, No. 6, pp. 489–504, 2001. [DOI:10.1007/BF02943234]
2. [2] H. Nakayama, Y. Hattori and R. Ishii, "Rule Extraction Based on Rough Set Theory and Its Application to Medical Data Analysis," IEEE Conference on Data Analysis 0-7803-5731-0/99, pp.924-929, 1999. [DOI:10.1109/ICSMC.1999.815677]
3. [3] Z. Pawlak, "Rough Sets: Theoretical Aspects of Reasoning about Data," Kluwer Academic Publishers, Dordrecht, Boston, London, 1991. [DOI:10.1007/978-94-011-3534-4]
4. [4] Z. Pawlak, "Rough Sets and Data Analysis," IEEE Conference Intelligent Processing Systems, Beijing, China, October 28-31, 1996. [DOI:10.1109/AFSS.1996.583540]
5. [5] A.S. Honby, Oxford Advanced Learners Dictionary of Current English," Oxford University Press, UK, 1974.
6. [6] Q. Zhang, Z. Han and F. Wen, "A New Approach for Fault Diagnosis in Power Systems Based on Rough Set Theory," 4th international Conference on Power Systems Control, Operation and Management, APSCOM-97, Hong Kong, November 1997, pp.597-602, 1997. [DOI:10.1049/cp:19971902]
7. [7] M. Toshinori, "Rough Control Application of Rough Set Theory to Control," Computer and Information Science Department Cleveland State University, 1996. [PMID]
8. [8] W. Ziarko, "The Discovery, Analysis and Representation of Data Dependencies in Databases," Knowledge Discovery in Databases, AAAI MIT Press, Cambridge, MA, pp.213-228, 1993.
9. [9] L. Xiaolei and W. Xiaobing, "The Application of Rough Set Theory in Vehicle Transmission System Fault Diagnosis," IEEE International Vehicle, ISSN:0-7803-5296-3/99,1999, pp.240-242, 1999. [DOI:10.1109/IVEC.1999.830674]
10. [10] S. Surekha, and G. Jaya Suma, "Swarm Intelligence and Variable Precision Rough Set Model: A Hybrid Approach for Classification." Computational Intelligence Techniques in Health Care Part of the series Springer Briefs in Applied Sciences and Technology, pp. 83-94, 2016.
11. [11] P. Langley, "Selection of relevant features in machine learning." AAAI Fall Symposium on Relevance, pp. 1-5, 1994. [DOI:10.21236/ADA292575]
12. [12] W. Siedlecki and J. Sklansky, "On automatic feature selection," International Journal of Pattern Recognition and Artificial Intelligence, Vol. 2, No. 2, pp. 197-220, 1988. [DOI:10.1142/S0218001488000145]
13. [13] A. Raze and G. Nasajyan, "Application of Rough Theory in Decision-Making Theory", Conference on Electrical Engineering, Gonabad Branch, Islamic Azad University, 2016.
14. [14] D. Chouchoulas and Q. Shen, "Rough set-aided keyword reduction for text cat-egorisation." Applied Artificial Intelligence, Vol. 15, No.9, pp. 843-873, 2001. [DOI:10.1080/088395101753210773]
15. [15] T. Beaubouef and R. Lang, "Rough Set Techniques for Uncertainty Management in Automated Story Generation," 36th Annual Conference on Southeast Regional Conference, April, pp.326-331, 1998.
16. [16] P. Hongxia, M. Qingfeng and W. Xiuye, "Research on Fault Diagnosis of Gearbox Based on Particle Swarm Optimization Algorithm," IEEE 3rd International Conference on Mechatronics, pp. 228-231, 2006. [DOI:10.1109/ICMECH.2006.252492]
17. [17] B. Xue, M. Zhang and W.N. Browne, "Particle Swarm Optimization for Feature election in Classification: A Multi-Objective Approach," IEEE Transactions on Cybernetics, Vol. 43, No. 6, pp. 56-71, 2013. [DOI:10.1109/TSMCB.2012.2227469] [PMID]
18. [18] C.O. Caio, D. Ramos, N.S. André, G. Chiachia, X. Alexandre and J.P. Papad, "A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection," Computers & Electrical Engineering, Vol. 37, pp. 886–894, 2011. [DOI:10.1016/j.compeleceng.2011.09.013]
19. [19] R. Diao and Q. Shen, "Feature Selection with Harmony Search," IEEE Systems, Man and Cybernetics Society, pp.128-135, 2012. [PMID]
20. [20] H. Tovhidi, H. Nezamabadi and S. sarozadi, "eature Selection Using Binary Ant colony Population Algorithm", Fisrt international conference on fuzzy systems, 2004.
21. [21] S. Kyanfar and M.R. Meybodi, "Provides an adaptive ant colony algorithm for solving continuous optimization problems", Fifth National Conference on Command and Control, 2010.
22. [22] K. Socha, and M. Dorigo, "Ant colony optimization for continuous domains," European Journal of Operational Research. Vol. 185, No. 3, pp. 1155-1173, 2008. [DOI:10.1016/j.ejor.2006.06.046]
23. [23] B. De la Iglesia, "Evolutionary computation for feature selection in classification problems." Data Mining and Knowledge Discovery, Vol. 3, No. 6, pp. 381–407, 2013. [DOI:10.1002/widm.1106]
24. [24] D. Jia, X. Duan and M.K. Khan, "Binary Artificial Bee Colony optimization using bitwise operation." Computer Industrial Engineering, Vol. 76, pp. 360–365, 2014. [DOI:10.1016/j.cie.2014.08.016]
25. [25] M. Mahdizadeh and M. Eftekhari, "A new fuzzy rules weighting approach based on Genetic Programming for imbalanced classification." JSDP. Vol. 11, No 2, pp. 111-125, 2015.
26. [26] M. Dorigo and G.D. Caro, "Ant colony optimization: A new meta-heuristic," Congress on Evolutionary Computing, pp. 17-26, 1999.
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