Graphical Representation of Multivariate Data
Graphical Representation of Multivariate Data
Guest Editorial: Special Section on Visual Analytics
IEEE Transactions on Visualization and Computer Graphics
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Journal of Biomedical Informatics
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Hi-index | 0.00 |
A novel Visual Combining Classifier (VCC), which integrates two-dimensional graphical representation of the attribute data, image processing and pattern recognition techniques together, has been proposed. The basic principle of the VCC is mapping attribute data of a data matrix to the twodimensional graphs, transforming these graphs to sub classifiers by pixel graphs, and combining the sub classifiers by decision rules. By interactive approaches, the optimum graphs for classification could be chosen and then pattern recognition could be realized automatically. The two experiments of the scatter and pole graphical representations based on Iris database have been made and classification precisions are 98.67% and 97.33% by LOOCV respectively.