Mixed Intelligent Systems by Tadeusz A. Grzeszczyk

Author:Tadeusz A. Grzeszczyk
Language: eng
Format: epub, pdf
Publisher: Springer International Publishing, Cham

Projects

Criterion c 1

Criterion c 2

…

Criterion c m

Decision

x 1

f(x 1 , c 1 )

f(x 1 , c 2 )

…

f(x 1 , c m )

d 1

x 2

f(x 2 , c 1 )

f(x 2 , c 2 )

…

f(x 2 , c m )

d 2

…

…

…

…

…

x n

f(x n , c 1 )

f(x n , c 2 )

…

f(x n , c m )

d n

Source: Based on Grzeszczyk (2017)

Projects from training sets stored in a decision table with project evaluation criteria and a decision attribute create in this way the initial representation of symbolic knowledge, which is subject to modifications as a result of the implementation of learning processes. The effect of this kind of processes is a set of generated rules, which are useful for classifying new projects not previously saved in the system.

The basic concept associated with rough set theory is approximation, which enables the representation and processing of vague concepts. To describe each of these kinds of concepts, two approximations (sharp concepts) called lower and upper approximations are necessary.

Figure 4.4 depicts the line specifying an exemplary shape of rough set that runs in an area referred to as a boundary region. It is an area reflecting a sort of indeterminacy and uncertainty.

Fig. 4.4Project evaluation and granular computing. (Source: Based on Pawlak (1991) and Grzeszczyk (2013))

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