Abstract
A novel membership function makes class memberships of outliers less clear-cut and thus resolve the problem of classification based on normal populations or normal mixtures. The membership function is used for classification problems in a manner called as post supervised.
We apply the fuzzy classifier to the combustion images obtained from a video camera focusing at the combustion and melting zone of a melting furnace. The algorithm takes about 30 seconds for a 10-fold
cross validation on 2,000 images with 20 PCA basis vectors. The classifier is order of magnitude faster than the k nearest neighbor (k-NN) algorithm.
