OWA Based Information Fusion Techniques for Classification Problem
Ching‐Hsue Cheng, Jingwei Liu, Ming‐Chang Wu · 2007
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.