A novel data selection method based on shadowed sets

作者：Zhou Yu^*; Su Haibin; Zhang Hongtao

来源：International Conference on Advanced in Control Engineering and Information Science (CEIS), 2011-08-18 to 2011-08-19.

DOI：10.1016/j.proeng.2011.08.261

• Data selection
• neural network (NN)
• shadowed sets
• supervised learning

One of the main factors that affect the supervised learning performance of neural network (NN) is training data set. However, for real world problems, it is not always easy to obtain high quality training data sets. In this paper, a novel training data selection method based on shadowed sets is proposed that can select an informative and representative subset of training data to ameliorate the supervised learning performance of NN. The main goal of this work is to improve the generalization ability and diminish misclassification errors of classifier of NN. This paper firstly introduces central idea of shadowed sets. Then the algorithm of proposed data selection method is described in detail. Finally, taking LVQ model as an example, some experiments are made to test validity of this method. The experiment results indicate that using selected sample data to train NN can lessen computation consumption, save training time, guarantee generalization ability, which verify the effectiveness and applicability of proposed data selection method.

• 出版日期2011
• 单位华北水利水电大学