Neural network classification in non-homogeneous feature space
Alexander Yu. Dorogov, V. Yu. Lesnykh · Optical Memory and Neural Networks · 2011
Data system analysis methods for designing of collective neural network classifiers are considered. It is suggested to use methods of sign graph local balancing and algorithms of system behavior stereotype selection for construction of competent areas of local classifiers. Connection graph is formed on the base of statistic dependences between variables of feature space. Decisions of local classifiers are integrated according to vote principle. Experimental results for real data base with a high degree of non-homogeneity are shown.