Machine Learning
Self-Organizing neural networks: recent advances and applications
Self-Organizing neural networks: recent advances and applications
Self-Organizing Maps
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Robustness analysis of the neural gas learning algorithm
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast self-organizing feature map algorithm
IEEE Transactions on Neural Networks
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One of the most important feature of the Neural Gas is its ability to preserve the topology in the projection of highly dimensional input spaces to lower dimensions vector quantizations. For this reason, the Neural Gas has proven to be a valuable tool in data mining applications.In this paper an incremental ensemble method for the combination of various Neural Gas models is proposed. Several models are trained with bootstrap samples of the data, the "codebooks" with similar Voronoi polygons are merged in one fused node and neighborhood relations are established by linking similar fused nodes. The aim of combining the Neural Gas is to improve the quality and robustness of the topological representation of the single model. We have called this model Fusion-NG.Computational experiments show that the Fusion-NGmodel effectively preserves the topology of the input space and improves the representation of the single Neural Gas model. Furthermore, the Fusion-NGexplicitly shows the neighborhood relations of it prototypes. We report the performance results using synthetic and real datasets, the latter obtained from a benchmark site.