A Rough Set Based Total Quality Management Approach in Higher Education - Complex System Modelling and Control Through Intelligent Soft Computations - page 394

Fig. 2 Lower and upper
approximation and accuracy
of classi cation

If

a P ð X Þ¼ 1

;

then the set is crisp with respect to P and if

a P ð X Þ \ 1

;

which means set is rough with respect to P.
The lower and upper approximation and the classi cation accuracy of decision

Table 3 are shown in Fig. 2 .

Decision Rules
Extracting decision rules from the decision table is one of the important aspects of
RST. Numbers of attribute reduction algorithm are available which can lead to more
accurate and simple decision rules. These decision rules can directly determine the
performance of information system. Decision rules are generally represented in the
form of form. Reduct based rules can also be generated which are lesser in

number and yet signi

' if-then '

cant (Vashist and Garg 2011 , 2012 ). The set of decision rules
are also called decision algorithm.
Heuristic rules for decision Table 3 as extracted by Rose 2 S/W are represented
as follows:

Rule 1 (a = 4) => (D = 1); [8, 8, 72.73 %, 100.00 %] [8, 0, 0, 0, 0] [{40, 41, 43,

44, 45, 46, 47, 48}, {}, {}, {}, {}] or

Rule 1 If (Effective Learning and Teaching = excellent) then(Grade = excellent).

Rule 2 (a = 1) & (c = 1) => (D = 5); [8, 8, 66.67 %, 100.00 %] [0, 0, 0, 0, 8][{},

{}, {}, {}, {28, 30, 31, 33, 34, 35, 36, 38}] or