Multi-dimensional Bayesian Network Classifiers Based on ICA
Tang Xing-ji · Electronic Science and Technology · 2014
Multi-dimensional Bayesian network classifiers( MBCs) are probabilistic graphical models proposed to deal with multi-dimensional classification problems,where each feature variable determines one or more than one class variable. For the problem of high dimensional of feature attributes and information redundancy,the Independent Component Analysis( ICA) is applied to decrease the dimension of feature variable which could completely describe data. Then,we construct a multi-dimensional Bayesian network classifier according to the decreased data. Finally,the performance of the MBCs is proved good by theoretical analysis. The experiment results show that for three benchmark multi-dimensional data sets,the multi-dimensional Bayesian network classifier based on ICA outperforms other algorithms in accuracy.