Affiliations: School of Information Science and Technology, Southwest Jiaotong Univ. 610031 Chengdu, China. {dengwb, hufeng}@cqupt.edu.cn | Inst. of Elect. Inform. Tech. Chongqing Inst. of Green and Intel. Tech., CAS, 401122 Chongqing, China. wanggy@cqupt.edu.cn | Institute of Computing Science, Poznań Univ. of Tech. 60-965 Poznań, Poland. {jblaszczynski, rslowinski, mszelag}@cs.put.poznan.pl
Note: [] Chinese authors wish to acknowledge the support of their National Natural Science Foundation of China under grant 61073146, Inter-governmental Science and Technology Cooperation of China and Poland under Grant 34-5, Natural Science Foundation Project of CQ CSTC under grant 2008BA2041, cstc2012jjA1649 and Chongqing Key Lab of Computer Network and Communication Technology under grant CY-CNCL-2010-05
Note: [] Address for correspondence: Chongqing Institute of Green and Intel. Tech., CAS, 401122 Chongqing, China Also works: Chongqing Key Lab. of Comp. Intel., Chongqing Univ. of Posts and Telecomm., 400065 Chongqing, China
Note: [] Polish authors wish to acknowledge the support from the Polish National Science Centre, grant N N519 441939 (R. Słowiński) and from the Poznań University of Technology, grant 91-516/DS-MLODA KADRA (J. Błaszczyński & M. Szeląg)
Note: [] Also works: Systems Research Institute, Polish Academy of Science, 01-447 Warsaw, Poland
Abstract: In order to handle inconsistencies in ordinal and monotonic information systems, several relaxed versions of the Dominance-based Rough Set Approach (DRSA) have been proposed, e.g., VC-DRSA. These versions use special consistency measures to admit some inconsistent objects in the lower approximations. The minimal consistency level that has to be attained by objects included in the lower approximations is defined using a prior knowledge or a trial-and-error procedure. In order to avoid dependence on prior knowledge, an alternative way of handling inconsistencies is to iteratively eliminate the most inconsistent objects (according to some measure) until the information system becomes consistent. This idea is a base of a new method of handling inconsistencies presented in this paper and called TIPStoC. The TIPStoC algorithm is illustrated by an example from the area of telecommunication and the efficiency of the new method is proved by a computational experiment.
Keywords: Dominance-based Rough Set Approach, inconsistency, inconsistency measure, ordinal classification with monotonicity constraints, decision rules
