the data set above the calculations are information gain 80 for vendor name 20 initial entropy u2212 total entropy after knowledge of vendorname information gain 81 for gl description 21 initial entropy u2212 total entropy after knowledge of gl description 0 4515 u2212 0 3524 0 0991 static weights to attributes overall attribute conditional information normalized entropy name entropy gain weights 0 4515 vendor name 0 1505 0 3010 1 5046 gl description 0 3524 0 0991 0 4954 get interval for variation about static weights 50 51 input data set 16 above static weights 50 51 of each attribute 20 21 output interval of variation for each attribute 20 21 in the data set 16 1 for each attribute i 20 21 with static weight 50 51 in the data set 16 a find conditional entropy of each of the descriptors or values 32 33 and 24 26 for current attribute 20 21 b note since conditional entropy indicates uncertainty about classification given some attribute value or descriptor the lower the conditional entropy the lower would be uncertainty and hence more important would be the value or descriptor of attribute thus the contribution of this value of attribute in the corresponding attribute 39 s importance is inversely proportional to the conditional entropy one way to achieve this is to make attribute value importance score 85 86 as an inverse of the corresponding.