QUANTIFICATION OF VAGUENESS IN MULTICLASS CLASSIFICATION BASED ON MULTIPLE BINARY NEURAL NETWORKS

摘要：

This paper presents an innovative approach to solve the problem of multiclass classification.One-against-one neural networks are applied to interval neutrosophic sets (INS).INS associates a set of truth, false and indeterminacy membership values with an output.Multiple pairs of the truth binary neural network and the false binary neural network are trained to predict multiple pairs of the truth and false membership values.The difference between each pair of truth and false membership values is considered as vagueness in the classification and formed as the indeterminacy membership value.The three memberships obtained from each pair of networks constitute an interval neutrosophic set.Multiple interval neutrosophic sets are then created and used to support decision making in multiclass classification.We have applied our technique to three classical benchmark problems including balance, wine, and yeast from the UCI machine learning repository.Our approach has improved classification performance compared to an existing one-against-one technique which applies only to the truth membership values.

关键词： Multiclass classification Feed-forward backpropagation neural network Vagueness

作者: PAWALAI KRAIPEERAPUN CHUN CHE FUNG KOK WAI WONG

作者单位: School of Information Technology, Murdoch University South Street, Murdoch, 6150, Western Australia

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 140-144

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）