Classification of process data and images by human assisted fuzzy similarity analysis
Gancho L. Vachkov, Hidenori Ishihara · 2009 ICCAS-SICE · 2009
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.