Variable Precision Rough Computing is certainly a better tool than Rough Computing. But, I could not find more research papers on VPRS. What are the drawbacks in VPRS?
This is a good question with many paths to follow in looking for answers.
A recent study of variable precision rough sets (VPRS) and its application is given in
B. Griffiths, A Variable Precision Rough Set Theory Decision Support System: With an Application to Bank Rating Prediction, Ph.D. thesis, Cardiff University, 2008:
http://orca.cf.ac.uk/55175/1/U585564.pdf
See Chapter 2, starting on page 22, for an introduction to VPRS. Two useful Propositions introduced by W. Ziarko are given in Section 2.3, starting on page 40.
A good place to look is
K. Thangavel, Q. Shen, A. Pethalakshmi, Application of clustering for feature selection based on rough set theory approach, AIML Journal 6, 2006, no. 1, 19-27:
https://wiki.eecs.yorku.ca/course_archive/2010-11/W/4403/_media/p1120548004.pdf
See Section 2.3.2, starting on page 23.
I agree the VPRS model has advantages but the use of it is more complex than the classic approach of rough sets due his probabilistic approach.
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