Fundamentals of algorithmics
Machine Learning
Measures for the organization of self-organizing maps
Self-Organizing neural networks
Self-Organizing Maps
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Neural Computation
Obtaining Accurate Neural Network Ensembles
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Application of Topology Preserving Ensembles for Sensory Assessment in the Food Industry
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Fusion of self organizing maps
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
ViSOM ensembles for visualization and classification
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Boosting unsupervised competitive learning ensembles
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Visualization of Tree-Structured Data Through Generative Topographic Mapping
IEEE Transactions on Neural Networks
Pattern Recognition Letters
Clustering ensemble for spam filtering
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Evolutionary optimization of regression model ensembles in steel-making process
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Enhanced self organized dynamic tree neural network
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A bio-inspired fusion method for data visualization
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
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Weighted Voting Superposition is a novel summarization algorithm for the results of an ensemble of Self-Organizing Maps. Its principal aim is to achieve the lowest topographic error in the map in order to obtain the best possible visualization of the internal structure of the data sets under study. This is done by means of a weighted voting process between the neurons of the ensemble maps in order to determine the characteristics of the neurons in the resulting map. The algorithm is applied in this case to the most widely known topology preserving mapping architecture: the Self- Organizing Map. A comparison is made between the novel fusion algorithm presented in this work and other previously devised fusion algorithms, along with a new variation of those algorithms, called Ordered Similarity. Although a practical example of the new algorithm was introduced in an earlier work, a rigorous description and analysis is presented here for the first time by comparing the performance of the aforementioned algorithms in relation to three well-known data sets (Iris, Wisconsin Breast Cancer and Wine) obtained from Internet repositories. The results show how this novel fusion algorithm outperforms the other fusion algorithms, yielding better visualization results for ensemble summarization of maps.