Soft granular computing based classification using hybrid fuzzy-KNN-SVM

Intelligent Decision Technologies

Volumn 10, Issue 2, 2016, Pages 115-128

  Panda, Mrutyunjaya ^a   Abraham, Ajith ^b,c   Tripathy, B K ^d  

^a UTKAL UNIVERSITY   (India)

^b SCIENTIFIC NETWORK FOR INNOVATION AND RESEARCH EXCELLENCE   (United States)

^c VSB TECHNICAL UNIVERSITY OF OSTRAVA   (Czech Republic)

^d VIT UNIVERSITY   (India)

Author keywords

discretization; Granular computing; hybrid model; statistical significance; supervised model; unsupervised model

Indexed keywords

ARTIFICIAL INTELLIGENCE; BENCHMARKING; COMPUTATION THEORY; FUZZY LOGIC; GRANULAR COMPUTING; GRANULATION; LEARNING SYSTEMS; SUPPORT VECTOR MACHINES;

DISCRETIZATIONS; HYBRID MODEL; INFORMATION GRANULATION; MINIMUM DESCRIPTION LENGTH; STATISTICAL SIGNIFICANCE; STATISTICAL SIGNIFICANCE TEST; SUPERVISED DISCRETIZATION; UCI MACHINE LEARNING REPOSITORY;

CLASSIFICATION (OF INFORMATION);

EID: 84960844134     PISSN: 18724981     EISSN: 18758843     Source Type: Journal    
DOI: 10.3233/IDT-150243     Document Type: Article

References (63)

1. A. Bargiela andW. Pedrycz, The roots of granular computing, Proc of the IEEE Int'l Conf on Granular Computing (2006), 806-809.
2. A. Bargiela and W. Pedrycyz, Towards a theory of granular computing for human-centered information processing, IEEE Transaction on Fuzzy Systems 16(2) (2008), 320-330.
3. A. Budihardjo, J. Grzymala-Busse and L. Woolery, Program LERS-LB 2.5 as a tool for knowledge acquisition in nursing, Proceedings of the 4th Int, Conference on Industrial and Engineering Applications of AI and Expert Systems (1991), 735-740.
4. A. Ganivada, S. Dutta and S.K. Pal, Fuzzy rough granular neural networks, Fuzzy Granules and Classification, Theoretical Computer Science, Elsevier 412 (2011), 5834-5853.
5. A. Ganivada, S.S. Ray and S.K. Pal, Fuzzy rough set and a granular neural network for unsupervised feature selection, Neural Networks, Elsevier 48 (2013), 91-108.
6. A. Kazem, E. Sharifi, F.K. Hussain, M. Saberi and O.K. Hussain, Support vector regression with chaos-based firefly algorithm for stock market price forecasting, Applied Soft Computing, Elsevier 13 (2013), 947-958.
7. A. Skowron and J. Stepaniuk, Information granules: Towards foundations of granular computing, International Journal of Intelligent Systems 16 (2001), 57-85.
8. C.H. Wan, L.H. Lee, R. Rajkumar and D. Isa, A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbour and support vector machine, Expert Systems with Applications 39 (2012), 11880-11888.
9. C. Lin, C. Yeh, S. Liang, J. Chung and N. Kumar, Supportvector-based fuzzy neural network for pattern classification, IEEE Transactions on Fuzzy Systems 14(1) (2006), 31-41.
10. D. Li, D. Miao and H. Zhang, The theory, model and method of granular computing, Journal of Fudan (Natural Science) 43(5) (2004), 837-841.
11. D. Li, D.Miao, D. Zhang et al., An overview of granular computing, Computer Science 32(9) (2005), 1-12.
12. D. Li, Z. Chen and J. Yin, A new attribute reduction recursive algorithm based on granular computing, Journal of Computers 8(3) (Mar 2013), 630-637.
13. S. Ding, L. Xu, H. Zhu and L. Zhang, Research and progress of cluster algorithms based on granular computing, International Journal of Digital Content Technology and Its Applications 4(5) (Aug 2010), 96-104.
14. E.A. Zanaty, Support vector machines (SVMs) versus multilayer perception (MLP) in data classification, Egyptian Informatics Journal 13 (2012), 177-183.
15. G. Panoutsos and M. Mahfouf, Discovering knowledge and modelling systems using granular computing and Neurofuzzy structures, in: NiSIS'05, European Symposium on Nature-Inspired Smart Information Systems, Albufeira, Portugal, (2005).
16. H. Huang, S. Ding, F. Jin, J. Yu and Y. Han, A novel granular support vector machine based on mixed kernel function, International Journal of Digital Content Technology and its Applications (JDCTA) 6(20) (2012), 484-492. doi: 104156/jdcta.vol6.issue20.52.
17. H. Li and S. Ding, Research and development of granular neural networks, Applied Mathematics and Information Sciences Journal 7(3) (2013), 1251-1261.
18. H. Liu, C. Liu and C.-A. Wu, Granular computing classification algorithms based on distance measures between granules from the view of set, Computational Intelligence and Neuro Science, Hindawii Publishing Corporation, (2014), ID-656790, 1-9
19. I. Kononenko and H.S. June, Attribute selection for modelling, Future Generation Computer Systems 13(2-3) (1997), 181-195.
20. I. Mitov, Kr. Ivanova, Kr. Markov, V. Velychko, K. Vanhoof and P. Stanchev, PaGaNe-A classification machine learning system based on the multidimensional numbered information spaces, in: Proc of Intelligent Systems and Knowledge Engineering, Hasselt, Belgium 11(27-28) (2009), 279-287.
21. I.U. Sikder, A granular computing approach to decision analysis using Rough set theory, in: Proc of the 2010 International Conference on Industrial Energy and Operations Management, Dhaka, Bangladesh, (Jan 9-10 2010). http://citeseerx. ist. psu. edu/viewdoc/summarydoi=101.1.397.6626.
22. J.C. Huehn and E. Huellermeier, FURIA: An algorithm for unordered fuzzy rule induction, Data Mining and Knowledge Discovery 19(3) (2009), 293-319.
23. J.D. Spurrier, On the null distribution of the kruskal-wallis statistic, Journal of Nonparametric Statistics 15(6) (2003), 685-691.
24. J. Dougherty, R. Kohavi and M. Sahami, Supervised and unsupervised discretization of continuous features, Proceedings of Twelfth International Conference on Machine Learning 95(10) (1995), 194-202.
25. J. Hu and C. Guan, An emotional agent model based on granular computing, Mathematical Problems in Engineering, Hindawii Publishing Corporation, (2012), Article ID 601295, doi:101155/2012/601295.
26. J.T. Yao and Y.Y. Yao, A granular computing approach to Machine Learning, in: Proc of the 1st International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'02) (2002), 732-736.
27. J.T. Yao, A ten year review of granular computing, in: Proc of IEEE International Conference on Granular Computing, silicon Valley, USA, (Nov 2-5 2007), 734-739.
28. L.A. Zadeh, Some reflections on information granulation and its centrality in granular computing, computing with words, the computational theory of perceptions and precisiated natural language, in: Proc Data Mining, Rough Sets and Granular Computing, Studies in Fuzziness and Soft Computing 95 (2002), 3-20.
29. L.A. Zadeh, Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems 19 (1997), 111-127.
30. L.A. Zadeh, Fuzzy sets and information granularity, in: Advances in Fuzzy Set Theory and Applications N. Gupta, R. Ragade and R. Yager, eds, North-Holland, Amsterdam, 1979, pp. 3-18.
31. L.A. Zadeh, Some reflections on soft computing, granular computing and their roles in the conception, design and utilization on information, Intelligent Systems, Soft Computing, Springer-Verlag 2 (1998), 23-25.
32. M.A. Mahmood, N. El-Bendary, A.E. Hassanien and H.A. Hefny, Classification Approach based on Rough Mereology, ISI-2013, India, 265-273.
33. M. Zhang and J.-X. Cheng, Pattern classification with granular computing, GESTS International Transaction on Computer Science and Engineering 20(1) (2005), 137-145.
34. M.G.C.A. Cimino, B. Lazzerini, F. Marcelloni and W. Pedrycyz, Genetic interval neural networks for granular data regression, Information Sciences, Elsevier 257(9) (2014), 313-330.
35. M.K. Ismail and V. Ciesielsk, An empirical investigation of the impact of discretization on common data distributions, design and application of hybrid intelligent systems, in: Proc of Third International Conference on Hybrid Intelligent Systems 105 (2003), 692-701.
36. N.B.H. Ahmad, S.M. Shamsuddin and A. Abraham, Granular mining of student's learning behavior in learning management system using rough set technique, Computational Intelligence for Tech Enhanced Learning, F. Xhafa et al., eds, Springer-Verlag Berlin Heidelberg, (SCI 273) (2010), 99-124.
37. P. Zhu and Q. Hu, Adaptive neighbourhood granularity selection and combination based on margin distribution optimization, Information Aciences, Elsevier 249 (2013), 1-12
38. R. Abraham, J.B. Simha and S.S. Iyengar, Effective discretization and hybrid feature selection using Naïve bayesian classifier for Medical data mining, International Journal of Computational Intelligence Research 5(2) (2009), 116-129.
39. R. Singh, M. Vatsa and A. Noore, Multiclass m-Granular soft support vector machine: A case study, in: Proceedings of International Conference on Pattern Recognition (2008), 1-4.
40. S.B. Devasenapati and K.I. Ramachandran, Random forest based misfire detection using kononenko discretiser, ICTACT Journal on Soft Computing 2(2) (2012), 270-275.
41. S. Ding and B. Qi, Research of granular support vector machine, Artif Intell Rev 38(1-7) (2012), Springer, DOI: 101007/s10462-011-9235-9.
42. S. Fong, O. Mohammed, J. Fiaidhi, S. Mohammed and C.K. Kwoh, Measuring similarity by prediction class between biomedical datasets via fuzzy unordered rule induction, International Journal of Bio-Science and Bio-Technology 6(2) (2014), 159-168. http://dxdoi.org/10.14257/ijbsbt.2014.6.2. 16.
43. S. Garca, J. Luengo, J.A. Saez, V. Lopez and F. Herrera, A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning, IEEE Transactions on Knowledge and Data Engineering 25(4) (2013), 734-750.
44. S. Kotsiantis, D. Kanellopoulos and D. Techniques, A recent survey, GESTS International Transactions on Computer Science and Engineering 32(1) (2006), 47-58.
45. S.K. Pal, Granular mining and rough-fuzzy pattern recognition: A way to natural computation, IEEE Intelligent Informatics Bulletin 13(1) (2012), 3-13.
46. S.K. Pal and S.K. Meher, Natural computing: A problem solving paradigm with granular information processing, Applied Soft Computing, Elsevier 13 (2013), 3944-3955.
47. S.K. Pal, B.U. Shankar and P. Mitra, Granular computing, rough entropy and object extraction, pattern recognition letters, Elsevier 26 (2005), 2509-2517.
48. T.Y. Lin, Data mining and machine oriented modelling: A granular computing approach, Journal of Applied Intelligence, Springer 13(2) (Oct 2000), 113-124.
49. U.M. Fayyad and K.B. Irani, Multi-interval discretization of continuous valued attributes for classification learning, Proceedings of the International Joint Conference on Uncertainity in Artificial Intelligence 2(1) (1993), 1022-1027.
50. W. Pedrycz, Granular computing, An Emerging Paradigm, ed, Physica-Verlag, Heidelberg, ISSN, (2001), 1434-9922.
51. W. Pedrycyz, Granular computing and logic processing in intelligent systems, AI-METH 2003-AI methods (Nov 5-7 2003), Gliwice, Poland, http://www.academia.edu/3056512/Granular-Computing-and-Logic-Processing-in-Intelligent-Systems.
52. W. Pedrycyz, Associations among information granules and their optimization in granulation-degranulation mechanism of granular computing, International Journal of Fuzzy Logic and Intelligent Systems 13(4) (Dec 2013), 245-253.
53. W. Wang, H. Guo, J.Y. Feng and J. Bi, Granular support vector machine based on mixed measure, Neuro Computing 101 (2013), 116-128.
54. Y. Chen, D. Miao and R. Wang, Outlier detection based on granular computing, in: RSCTC 2008, C.-C. Chan et al., eds, (LNAI 5306), Springer-Verlag, berlin, Heidelberg, (2008), 283-292.
55. Y. Tang, Y.-Q. Zhang, Z. Huang, X. Hu and Y. Zhao, Recursive fuzzy granulation for gene subsets extraction and cancer classification, IEEE Transaction on Information Technology in Biomedicine 12(6) (Nov 2008), 723-730.
56. Y. Wang, On system algebra: A denotational mathematical structure for abstract systems modelling, International Journal of Cognitive Informatics and Natural Intelligence, IGI Publishing, USA 2(2) (Apr 2008), 20-43.
57. Y.Wang, L.A. Zadeh and Y. Yao, On the system algebra foundations for granular computing, International Journal of Software Science and Computational Intelligence 1(1) (Jan-Mar 2009), 64-86.
58. Y.Y. Yao, A partition model of granular computing, Transactions on Rough Sets, Springer 1 (LNCS 3135) (2004), 232-253.
59. Y.Y. Yao, Granular computing: Basic issues and possible solutions, Proceedings of the 5th Joint Conference on Information Sciences (2000), 186-189.
60. Y.Y. Yao and N. Zhong, Granular computing using information tables, Data Mining, Rough Sets and Granular Computing Studies in Fuzziness and Soft Computing 95 (2002), 102-124.
61. Y. Yang and I.G.Webb, A comparative study of discretization methods for naïve bayes classifiers, in: Proceedings of PKAW, Japan, (2002), 159-173.
62. Z.I. Mooe and A. Inoue, Effectiveness of value granulation in machine learning for massively large and complex domain, in: Proc of IPMU 2008, L. Magdalena et al., eds, 22-27 Jun 2008, pp. 855-862.
63. Z. Pawlak, Granularity of knowledge, indiscernibility and rough sets, Proceedings of IEEE International Conference on Fuzzy Systems, (1998), 106-110.