Function to calculate the Correctness Rate, the Accuracy, the Ability to Seperate and the Confidence of
a classification rule.
Usage
ucpm(m, tc, ec = NULL)
Arguments
m
matrix of (scaled) membership values
tc
vector of true classes
ec
vector of estimated classes (only required if scaled membership values are used)
Value
A list with elements:
CR
Correctness Rate
AC
Accuracy
AS
Ability to Seperate
CF
Confidence
CFvec
Confidence for each (true) class
Details
The correctness rate is the estimator for the correctness of a classification rule (1-error rate).
The accuracy is based on the euclidean distances between (scaled) membership vectors and the vectors
representing the true class corner. These distances are standardized so that a measure of 1 is achieved
if all vectors lie in the correct corners and 0 if they all lie in the center.
Analougously, the ability to seperate is based on the distances between (scaled) membership
vectors and the vector representing the corresponding assigned class corner.
The confidence is the mean of the membership values of the assigned classes.
References
Garczarek, Ursula Maria (2002): Classification rules in standardized partition spaces.
Dissertation, University of Dortmund.
URL http://hdl.handle.net/2003/2789