Bayesian visual classification method based on parallel coordinates
Zhi Yang Gao · Computer Engineering and Applications Journal · 2008
This article combines the techniques of information visualization and machine learning to bring forward a Bayesian visual classification method based on parallel coordinates graphical representation of multivariate data.This method optimizes the parallel coordinates representation by class-conditional probability density estimations,then the transformed point score values are weighted and summed up to apply the Bayesian classification rules.This method makes the invisible data and algorithms visible by using parallel coordinates,consequently domain experts' knowledge is easier to be utilized,classification results are more interpretable so as to qualify it as an effective tool for some pattern recognition tasks such as medical diagnostics.