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In this paper, we modify the fuzzy ARTMAP (FA) neural network (NN) using the Bayesian framework in order to improve its classification accuracy while simultaneously reduce its category proliferation. The proposed algorithm, called Bayesian ARTMAP (BA), preserves the FA advantages and also enhances its performance by the following: (1) representing a category using a multidimensional Gaussian distribution, (2) allowing a category to grow or shrink, (3) limiting a category hypervolume, (4) using Bayes' decision theory for learning and inference, and (5) employing the probabilistic association between every category and a class in order to predict the class. In addition, the BA estimates the class posterior probability and thereby enables the introduction of loss and classification according to the minimum expected loss. Based on these characteristics and using synthetic and 20 real-world databases, we show that the BA outperformes the FA, either trained for one epoch or until completion, with respect to classification accuracy, sensitivity to statistical overlapping, learning curves, expected loss, and category proliferation.

We propose the recursive autonomy identification (RAI) algorithm for constraint-based (CB) Bayesian network structure learning. The RAI algorithm learns the structure by sequential application of conditional independence (CI) tests, edge direction and structure decomposition into autonomous sub-structures. The sequence of operations is performed recursively for each autonomous substructure while simultaneously increasing the order of the CI test. While other CB algorithms d-separate structures and then direct the resulted undirected graph, the RAI algorithm combines the two processes from the outset and along the procedure. By this means and due to structure decomposition, learning a structure using RAI requires a smaller number of CI tests of high orders. This reduces the complexity and run-time of the algorithm and increases the accuracy by diminishing the curse-of-dimensionality. When the RAI algorithm learned structures from databases representing synthetic problems, known networks and natural problems, it demonstrated superiority with respect to computational complexity, run-time, structural correctness and classification accuracy over the PC, Three Phase Dependency Analysis, Optimal Reinsertion, greedy search, Greedy Equivalence Search, Sparse Candidate, and Max-Min Hill-Climbing algorithms.

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Abstract-The application of neural networks (NN's) to automatic analysis of chromosome images is investigated in this paper. All aspects of the analysis, namely segmentation, feature description, selection and extraction, and classification, are studied. As part of the segmentation process, the separation of clusters of partially occluded chromosomes, which is the critical stage that state-of-the-art chromosome analyzers usually fail to accomplish, is performed. First, a moment representation of the image pixels is clustered to create a binary image without a need for threshold selection. Based on the binary image, lines connecting cut points imply possible separations. These hypotheses are verified by a multilayer perceptron (MLP) NN that classifies the two segments created by each separating line. Use of a classification-driven segmentation process gives very promising results without a need for shape modeling or an excessive use of heuristics. In addition, an NN implementation of Sammon's mapping using principal component based initialization is applied to feature extraction, significantly reducing the dimensionality of the feature space and allowing high classification capability. Finally, by applying MLP based hierarchical classification strategies to a well-explored chromosome database, we achieve a classification performance of 83.6%. This is higher than ever published on this database and an improvement of more than 10% in the error rate. Therefore, basing a chromosome analysis on the NN-based techniques that are developed in this research leads toward a completely automatic human chromosome analysis.