Rough-set belief rule model using multinomial subjective logic

Limin Jia; Wangning Ding; Hongqiang Jiao

Rough-set belief rule model using multinomial subjective logic

View Fulltext

Author(s): Limin Jia ¹ ; Wangning Ding ¹ ; Hongqiang Jiao ^{1, 2}
- Affiliations: 1: Institute of Information Engineering, Handan College, 056005 Handan, People's Republic of China;
  2: School of Management, Hebei University, 071002 Baoding, Hebei, People's Republic of China
Source: Volume 9, Issue 3, May 2015, p. 362 – 366
DOI: 10.1049/iet-smt.2014.0015 , Print ISSN 1751-8822, Online ISSN 1751-8830

Received 31/01/2014, Accepted 01/08/2014, Revised 08/06/2014, Published 20/02/2015

In accordance with the uncertainty of decision rules extraction process in boundary region of the rough set, this study proposes a rough-set belief rule model using multinomial subjective logic. The model builds the belief rule expectation for classification and recognition. It not only considered the effects of single condition attributes on decision, but also the multi-attributes. Finally, the example analysis and comparison with the R _0.5(X) model indicates the validity and advantage of this model.

References

1. 1)
  - 10. Ciucci, D., Dubois, D., Prade, H.: ‘Oppositions in rough set theory[M]//rough sets and knowledge technology’ (Springer, Berlin Heidelberg, 2012), pp. 504–513.
2. 2)
  - 23. Jøsang, A., Elouedi, Z.: ‘Redefining material implication with subjective logic’. Proc. of the 14th Int. Conf. on Information Fusion, Chicago, USA, 2011, pp. 1–6.
3. 3)
  - 4. Pawlak, Z., Skowron, A.: ‘Rough sets and Boolean reasoning’, Inf. Sci., 2007, 177, (1), pp. 41–73 (doi: 10.1016/j.ins.2006.06.007).
4. 4)
  - 16. Zhang, Q., Wang, G., Xiao, Y.: ‘Approximation sets of rough sets’, J. Softw., 2012, 23, (7), pp. 1745–1759 (doi: 10.3724/SP.J.1001.2012.04226).
5. 5)
  - 18. Jøsang, A., Hayward, R., Pope, S.: ‘Trust network analysis with subjective logic’. Proc. of the Twenty-Ninth Australasian Computer Science Conf., Hobart, Tasmania, Australia, 2006, vol. 48, pp. 85–94.
6. 6)
  - 13. Lee, Y., Tong, L.: ‘Predicting high or low transfer efficiency of photovoltaic systems using a novel hybrid methodology combining rough set theory, data envelopment analysis and genetic programming’, Energies, 2012, 5, pp. 545–560 (doi: 10.3390/en5030545).
7. 7)
  - 12. Bryan, B., Kazimierz, Z.: ‘Comparison of analytic hierarchy process and dominance-based rough set approach as multi-criteria decision aid methods for the selection of investment projects’, Am. J. Ind. Bus. Manage., 2012, 2, pp. 7–12.
8. 8)
  - 11. Zhang, M., Tang, Z., Xu, W., Min, Y.: ‘Variable multigranulation rough set model’, Pattem Recognit. Artif. Intell., 2012, 25, (4), pp. 709–720.
9. 9)
  - 2. Pawlak, Z.: ‘Rough set approach to knowledge-based decision support’, Eur. J. Oper. Res., 1997, 99, (1), pp. 48–57 (doi: 10.1016/S0377-2217(96)00382-7).
10. 10)
  - 9. Zhang, X., Mo, Z., Xiong, F.: ‘Rough set model based on logical difference operation of precision and grade and its algorithms’, Syst. Eng., Theory Pract., 2011, 31, (12), pp. 2394–2399.
11. 11)
  - 22. Jøsang, A., Diaz, J., Rifqi, M.: ‘Cumulative and averaging fusion of beliefs’, Inf. Fusion, 2010, 11, (2), pp. 192–200 (doi: 10.1016/j.inffus.2009.05.005).
12. 12)
  - 19. Jøsang, A., Ismail, R., Boyd, C.: ‘A survey of trust and reputation systems for online service provision’, Decis. Support Syst., 2007, 43, (2), pp. 618–644 (doi: 10.1016/j.dss.2005.05.019).
13. 13)
  - 7. Thangavel, K., Pethalakshmi, A., Jaganathan, P.: ‘A novel reduct algorithm for dimensionality reduction with missing values based on rough set theory’, Int. J. Soft Comput., 2006, 1, (2), pp. 111–117.
14. 14)
  - Z. Pawlak . Rough sets. Int. J. Comput. Inf. Sci. , 5 , 341 - 356
15. 15)
  - 8. Qian, Y., Liang, J., Dang, C.: ‘Incomplete multigranulation rough set’, IEEE Trans. Syst. Man Cybern. A, Syst, Hum., 2010, 40, (2), pp. 420–431 (doi: 10.1109/TSMCA.2009.2035436).
16. 16)
  - 20. Jøsang, A.: ‘Conditional reasoning with subjective logic’, Mult.-Valued Log. Soft Comput., 2008, 15, (1), pp. 5–38.
17. 17)
  - 14. Hasan, M., Shi, X.F., Tsegaye, T.: ‘Rainfall prediction model improvement by fuzzy set theory’, J. Water Resour. Prot., 2013, 5, pp. 1–11 (doi: 10.4236/jwarp.2013.51001).
18. 18)
  - 15. Elheishy, S.S., Saleh, A.A., Asem, A.: ‘A rough set and GIS based approach for selecting suitable shelters during an evacuation process’, J. Geogr. Inf. Syst., 2013, 5, pp. 1–12.
19. 19)
  - 5. Xie, H., Cheng, H., Niu, D.: ‘Discretization of continuous attributes in rough set theory based on information entropy’, Chin. J. Comput., 2005, 28, (9), pp. 1570–1574.
20. 20)
  - 3. Pawlak, Z., Skowron, A.: ‘Rough sets: some extensions’, Inf. Sci., 2007, 177, pp. 28–40 (doi: 10.1016/j.ins.2006.06.006).
21. 21)
  - 17. Jøsang, A.: ‘A logic for uncertain probabilities’, Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 2001, 9, (3), pp. 1–31 (doi: 10.1142/S0218488501000831).
22. 22)
  - 21. Jøsang, A., Quattrociocchi, W.: ‘Advanced features in Bayesian reputation systems’, Comput. Sci., 2009, 5695, (1), pp. 105–114.
23. 23)
  - 6. Qian, Y., Liang, J., Pedrycz, W., Dang, C.: ‘Positive approximation: an accelerator for attribute reduction in rough set theory’, Artif. Intell., 2010, 174, (9), pp. 597–618 (doi: 10.1016/j.artint.2010.04.018).

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Rough-set belief rule model using multinomial subjective logic

References

Related content