Data-driven fuzzy clustering based on maximum entropy principle and PSO
Expert Systems with Applications: An International Journal
International Journal of Innovative Computing and Applications
Dialectical non-supervised image classification
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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The materialist dialectical method is a philosophical investigative method to analyze aspects of reality. The aspects are viewed as complex complex processes composed by basic units named poles, which interact with each other. Dialectics has experienced considerable progress in the 19th and 20th century, with the works of Hegel, Marx, Engels and GRamsci in Philosophy and Economics. The movement of poles through their contradictions is viewed as a dynamic process with intertwined phases of evolution and revolutionary crisis. In order to build a computational process based on dialectics, the interaction between poles can be modeled using fuzzy membership functions. Based on this assumption, we introduce the Objective Dialectical Classifier (ODC), a non-supervised map for classification based on materialist dialectics and designed as an extension of fuzzy c-means classifier. As a case study, we used ODC to classify 181 magnetic resonance synthetic multispectral images composed by proton density, $T_1$- and $T_2$-weighted synthetic brain images. Comparing ODC to k-means, fuzzy c-means, and Kohonen's self-organized maps, concerning with image fidelity indexes as estimatives of quantization distortion, we proved that ODC can reach almost the same quantization performance as optimal non-supervised classifiers like Kohonen's self-organized maps.