OWA based information fusion method with PCA preprocessing for data classification

Information is getting more and more today, how to handle high dimensions data and high complexity data are the key issues of this contribution. Multi-attribute data usually possesses high data dimension and high data complexity. In order to solve these problems, the contribution proposes a new information fusion model which is briefly described as follows: (1) Reduce data dimensions by principal components analysis (PCA) method. (2) Calculate integrated values by order weighted averaging (OWA) operator. (3) Cluster data instance into specific group and train classification accuracy to obtain the best situation parameter alpha. (4) Validate classification accuracy from testing data. In the research, there are two datasets adopted to verify performances of proposed model, i.e. Iris dataset and Wisconsin Breast Cancer dataset. The experiments results show that classification accuracies of proposed model obviously surpass the listing methods.