抄録
Since different types of classifiers work best for
different types of data, our approach is to parameterize the
classifier and tailor them to individual data set. In the proposed
fuzzy c-means (FCM) classifier, parameter values are chosen by
evolutionary algorithms such as the particle swarm optimization
(PSO). The free parameters of membership functions are optimized
in order to increase true positive rate or true positive
count. The golden section search is applied to select a cut-off
point, which realizes a preselected positive count or false positive
count. This makes classification decisions more pragmatic than
those by the standard methods with missclassification rates or
loss functions.
