Data classification based on attribute vectorization and evidence fusion

Classifiers based on evidential reasoning (ER) rule can well handle the uncertainty in the mapping relationship between input attributes and output classes. To avoid the number of model parameters increasing with the growing number of input attributes, this paper proposes a classification model based on attribute vectorization and evidential reasoning (AV-ER). Firstly, different input attributes are combined into attribute vectors by using principal component analysis (PCA). Then, all training samples are casted into reference attribute vectors , and the reference evidence matrix (REM) is generated by likelihood function normalization. After that, all pieces of activated evidence are fused through ER theory to generate the final classification decision. In the fusion process, parameters of the initial classification model are optimized by genetic algorithm (GA), and Akaike information criterion (AIC) is used to evaluate the model performance comprehensively considering the model complexity and classification accuracy. Finally, typical UCI benchmark datasets are applied to verify the proposed AV-ER classification model, and the results indicate that the classification performance of the AV-ER model is satisfying while the number of the model parameters decrease obviously as well. •Attribute vectors are used as the input of a classifier to reduce the model complexity.•The importance of every attribute is determined by calculating its contribution to the main components in PCA.•AIC is used as the optimized objective function to keep a balance between classifier accuracy and model complexity.