OWA Based Information Fusion Techniques for Classification Problem

In this paper, we fusion multi-attribute data into the aggregated values of single attribute by OWA operators, and cluster the aggregated values for classification tasks. The proposed method is consisted of four steps: (1) use stepwise regression to selection the important attribute, (2) utilize OWA operator to get aggregated values of single attribute from multi-attribute data, (3) cluster the aggregated values by K-Means method, (4) predict the testing data´s classes. For verifying, we use two dataset to illustrate the proposed method, and compare with the listing methods. The datasets, one is Iris dataset; the other is Wisconsin-breast-cancer dataset. At last, the result shows that the proposed method is better than the listing methods.