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
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Data visualisation and manifold mapping using the ViSOM
Neural Networks - New developments in self-organizing maps
Clustering ensembles of neural network models
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
An Adaptive Associative Memory Principle
IEEE Transactions on Computers
Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
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
Data management by self-organizing maps
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
A weighted voting summarization of SOM ensembles
Data Mining and Knowledge Discovery
Content based image retrieval using a bootstrapped SOM network
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
Clustering ensemble for spam filtering
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Privacy-by-design rules in face recognition system
Neurocomputing
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This study presents a novel version of the Visualization Induced Self-Organizing Map based on the application of a new fusion algorithm for summarizing the results of an ensemble of topology-preserving mapping models. The algorithm is referred to as Weighted Voting Superposition (WeVoS). Its main feature is the preservation of the topology of the map, in order to obtain the most accurate possible visualization of the data sets under study. To do so, a weighted voting process between the units of the maps in the ensemble takes place, in order to determine the characteristics of the units of the resulting map. Several different quality measures are applied to this novel neural architecture known as WeVoS-ViSOM and the results are analyzed, so as to present a thorough study of its capabilities. To complete the study, it has also been compared with the well-know SOM and its fusion version, with the WeVoS-SOM and with two other previously devised fusion Fusion by Euclidean Distance and Fusion by Voronoi Polygon Similarity-based on the analysis of the same quality measures in order to present a complete analysis of its capabilities. All three summarization methods were applied to three widely used data sets from the UCI Repository. A rigorous performance analysis clearly demonstrates that the novel fusion algorithm outperforms the other single and summarization methods in terms of data sets visualization.