A Novel Visual Combining Classifier Based on a Two-dimensional Graphical Representation of the Attribute Data
Tao Zhang, Hong Wenxue · 2009
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 two-dimensional 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.