Biomedical pattern classification using an optimized fuzzy adaptive logic network
N. Retnakaran, Nick J. Pizzi · 2006
Our research focuses on the classification of biomedical data using optimized fuzzy adaptive logic networks (FALN). The FALN exploits the advantages of geometric representations of data and fuzzy logic-oriented operations on those representations. The construction of an optimized FALN involves the selection of a heterogeneous structure of varied learning architectures (for example, multilayer perceptrons (MLP) and radial basis functions (RBF)) for the geometry-based processing subsystem. Parametric gradient-based learning is adequate for optimizing an individual MLP, since the backpropagation of the error may be described using a differentiate function. However, gradient-based learning cannot be used for the learning of an optimal heterogeneous structure. One possible solution to this structure optimization problem is to use a genetic algorithm (GA) to find an optimal combination of MLPs and RBFs. This paper presents a solution to the FALN structure optimization problem using a GA approach. The performance of the optimized FALN is demonstrated by classifying a complex biomedical dataset comprising of 186 infrared spectra of synovial joint fluid.