Intelligent systemAn extended rule-based inference for general decision-making problems
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Cited by (17)
Belief rule-based classification system: Extension of FRBCS in belief functions framework
2015, Information SciencesCitation Excerpt :The belief functions theory, also called Dempster-Shafer theory, proposed and developed by Dempster [13] and Shafer [46] et al., has become one of the most powerful frameworks for uncertain modeling and reasoning. As fuzzy sets theory is well suited to dealing with fuzziness, and belief functions theory provides an ideal framework for handling imprecision and incompleteness, many researchers have investigated the relationship between fuzzy sets theory and belief functions theory and suggested different methods of integrating them [6,7,34,56,57]. Among these methods, Yang et al. [57] extended the fuzzy rule in belief functions theory and proposed a new knowledge representation scheme in a belief rule structure, which is capable of capturing fuzzy, imprecise, and incomplete causal relationships.
Hybrid data in the multiobjective evaluation of investments
2014, Procedia Computer ScienceMaximal confidence intervals of the interval-valued belief structure and applications
2011, Information SciencesCitation Excerpt :The DS theory is of fundamental importance in artificial intelligence and cognitive science. It has been applied in a wide range of areas including information fusion [7,74,82,96], expert systems [1,13,18,75,92], pattern recognition and classification [15–17,23–25,30,48,70], diagnosis and reasoning [10,21,36–38,51,52,54,55,69], knowledge reduction [83–85], decision analysis [8,11–14,46,74,80,86,88,90,93–95], intelligent control [33], process monitoring [71–73], audit risk assessment [31,32,40,41,63–68], environmental impact assessment [79], image processing [9,19,43,44,49,56], geographic information system [47], contractor selection [61,62], organizational self-assessment [58,91], safety analysis [45,78] and regression analysis [50,53]. In general, applying DS involves three fundamental steps: first, formulate mass functions (or basic belief assignments, BBAs) to represent uncertainties existing in the problem; second, compute belief functions on the basis of the mass functions; and finally, if necessary, combine mass functions into a single function.
On the dynamic evidential reasoning algorithm for fault prediction
2011, Expert Systems with ApplicationsCitation Excerpt :Dempster–Shafer (D–S) theory of evidence. Due to the power of the D–S theory in handling uncertainties (Bauer, 1997; Beynon, Cosker, & Marshall, 2001, 2002a; Chen, 1997; Yager, 1992; Yen, 1990), so far, it has found wide applications in many areas such as expert systems (Biswas, Oliff, & Sen, 1988; Beynon et al., 2001; Chen, 1997; Wallery, 1996), uncertainty reasoning (Benferhat, SafHotti, & Smets, 2000; George & Pal, 1996; Hullermeier, 2001; Ishizuka, Fu, & Yao, 1982; Jones, Lowe, & Harrison, 2002; Rakar, Juricic, & Ball, 1999), pattern classification (Binaghi & Madella, 1999; Binaghi, Gallo, & Madella, 2000; Denoeux, 1997, 1999, 2000a, 2000b; Denoeux & Zouhal, 2001; Denoeux & Masson, 2004), fault diagnosis and detection (Fan and Zuo, 2006a, 2006b; Parikh and Pont, 2001; Rakar and Juricic, 2002; Yang and Kim, 2006), information fusion (Fabre et al., 2001; Ruthven and Lalmas, 2002; Telmoudi and Chakhar, 2004), Multiple attribute decision analysis (Beynon et al., 2001; Beynon, 2002a, 2002b; Guo, Yang, & Chin, in press; Xu, Yang, & Wang, 2006; Yang & Xu, 2002a, 2002b; Yang & Sen, 1994; Yang, Liu, Wang, Sii, & Wang, 2006a; Yang, Wang, Xu, & Chin, 2006b), image processing (Bloch, 1996; Huber, 2001; Krishnapuram, 1991) and regression analysis (Monney, 2003; Pent-Renaud and Denoeux, 2004; Wang and Elhag, 2007). In the last decade, an evidential reasoning (ER) approach has been developed for MADA under uncertainty (Xu & Yang, 2003; Xu et al., 2006; Yang & Sen, 1994; Yang & Xu, 2002a, 2002b, 2004; Yang et al., 2006a, 2006b).
On the combination and normalization of interval-valued belief structures
2007, Information SciencesThe evidential reasoning approach for multiple attribute decision analysis using interval belief degrees
2006, European Journal of Operational Research