A Novel Improvement of Neural Network Classification Using Further Division of Partition Space

Authors:
Lin Wang;Bo Yang;Zhenxiang Chen;Ajith Abraham;Lizhi Peng
Affiliations:
School of Information Science and Engineering, University of Jinan, Jinan, China;School of Information Science and Engineering, University of Jinan, Jinan, China;School of Information Science and Engineering, University of Jinan, Jinan, China;Centre for Quantifiable Quality of Service in Communication Systems, Norwegian University of Science and Technology, Norway;School of Information Science and Engineering, University of Jinan, Jinan, China
Venue:
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Year:
2007

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Abstract

Further Division of Partition Space (FDPS) is a novel technique for neural network classification. Partition space is a space that is used to categorize data sample after sample, which are mapped by neural network learning. The data partition space, which are divided manually into few parts to categorize samples, can be considered as a line segment in the traditional neural network classification. It is proposed that the performance of neural network classification could be improved by using FDPS. In addition, the data partition space are to be divided into many partitions, which will attach to different classes automatically. Experiment results have shown that this method has favorable performance especially with respect to the optimization speed and the accuracy of classified samples.