Classification of process data and images by human assisted fuzzy similarity analysis

In this paper an incremental classification scheme for large data sets and images is proposed in the form of a two-stage computation scheme. First, information compression of the original data set or pixels is performed by a modification of the neural-gas unsupervised learning algorithms. Then two features are extracted from the obtained compressed information model, namely the center-of-gravity of the model and its size, which are further used in a fuzzy inference procedure for similarity analysis. The tuning of the membership functions parameters in the procedure for fuzzy similarity analysis is also discussed in the paper by using a modified particle swarm optimization algorithm that takes into account the predefined human preferences. Finally, the applicability of the proposed classification scheme is illustrated on a test example of 16 images.