Description Usage Arguments Details Value References Examples
This function is used as the second step in weight.entropy
for
calculating a decision weight for each attr
in the decision
matrix. The methodology of the entropy method [2] for determining the weights
out of a decision matrix is given by references [1] and [3]. See Details.
1 | entropy(normalizedMatrix)
|
normalizedMatrix |
a numeric matrix. If indeed normalized it should only
contain values between |
The sum of the output of this functions should always equal 1.
Contrasting with differenceToIdeal
small differences
between value attributes are rewarded a lower value and thus a relative
lower weight.
Note: the normalizing function used here normalize.sum
has
one limitation, if within a same attribute there are negative and positive
values, the function will likely produce a NaN
.
a decision weight (numeric vector with a sum of 1)
[1]Hwang, C. L., & Yoon, K. (2012). Multiple attribute decision making: methods and applications a state-of-the-art survey (Vol. 186). Springer Science & Business Media.
[2]Shannon, C. E. (2001). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5(1), 35.
[3]Lotfi, F. H., and Fallahnejad, R. (2010). Imprecise Shannons entropy and multi attribute decision making. Entropy, 12(1), 53-62.
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