As a powerful approach to data reasoning, rough set theory has
proven to be invaluable in knowledge acquisition, decision analysis
and forecasting, and knowledge discovery. With the ability to
enhance the advantages of other soft technology theories, hybrid
rough set theory is quickly emerging as a method of choice for
decision making under uncertain conditions.
Keeping the complicated mathematics to a minimum, Hybrid Rough
Sets and Applications in Uncertain Decision-Making provides a
systematic introduction to the methods and application of the
hybridization for rough set theory with other related soft
technology theories, including probability, grey systems, fuzzy
sets, and artificial neural networks. It also:
- Addresses the variety of uncertainties that can arise in the
practical application of knowledge representation systems
- Unveils a novel hybrid model of probability and rough sets
- Introduces grey variable precision rough set models
- Analyzes the advantages and disadvantages of various practical
applications
The authors examine the scope of application of the rough set
theory and discuss how the combination of variable precision rough
sets and dominance relations can produce probabilistic preference
rules out of preference attribute decision tables of preference
actions. Complete with numerous cases that illustrate the specific
application of hybrid methods, the text adopts the latest
achievements in the theory, method, and application of rough
sets.
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