Variable precision rough set model
Journal of Computer and System Sciences
International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Reduction of categorical and numerical attribute values for understandability of data and rules
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Stochastic approach to rough set theory
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
A new treatment and viewpoint of information tables
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
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In this paper, we discuss the most suitable “representation granularity”, keeping several types of discernibility including individually discernibility and class discernibility. In the traditional “reduction” sense, the goal is to find the smallest number of attributes such that they enable us to discern each tuple or each decision class. However, once we pay attention to the number of attribute values too, that is, the size of each attribute, another criterion is needed. Indeed, we should ask ourselves about which one is better in the following two situations: 1) we can discern them with a single attribute of size ten, and 2) we can do this with two attributes of size five. This study answers this question with some criteria. Especially, we deal with continuous attributes. If we evaluate this difference in the light of understandability, we may prefer the latter, because they give more simple descriptions. Such a combination of simple nominal description helps us as a language or as a Kansei representation. To do this, we propose some criteria and algorithms to find near-optimal solutions for those criteria. In addition, we show some results for some databases in UCI Machine Learning Repository.