Classification regions underlie attribute reduction in rough set theory, and the quantitative and extended ones become a research emphasis. According to the decision-theoretic rough set, this paper mainly proposes a new type of classification region in the two-category level, and further conducts relevant comparison analyses with the two usual types. By set regions, a new type of classification region is naturally proposed in the two-category decision-theoretic rough set; then, degeneration studies are made for two existing types; furthermore, the relevant comparison analyses are conducted among the three types, and the advantage of the proposed type is exhibited; finally, a concrete example is provided for detailed illustration. In particular, the enlargement mechanism of classification-positive region is also analyzed for the three types by comparison with the variable precision rough set and bayesian rough set. The new type has the extended feature for the classical classification regions, becomes more closely related to the basic structure of set regions, and exhibits improvements for the previous two types.
Key words
artificial intelligence /
rough set theory /
decision-theoretic rough set /
classification region /
set region /
quantitative extension
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