Title: Labelled decision-making method based on neural network model and pruning algorithm

Authors: Jian-hai Du

Addresses: State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China

Abstract: Aiming at the advantages in commonly used method of generating decision tree, a method of generating cost-sensitive decision tree (CSDT) based on the correlation degree of neural network attributes is proposed through quoting the correlation degree of neural network attributes and cost-sensitive learning. This method reduces the condition attributes by using rough set theory, and it takes the correlation degree and cost performance of attributes as the bases of split node to build the CSDT by using modified information gain method during the process of building decision tree. It is shown in test result that such method is superior to commonly used method of generating decision tree in classification accuracy and the number of nodes generated.

Keywords: neural network; rough set; cost sensitive; labelled tree reduction; decision making.

DOI: 10.1504/IJISTA.2018.095089

International Journal of Intelligent Systems Technologies and Applications, 2018 Vol.17 No.4, pp.497 - 506

Received: 16 May 2017
Accepted: 07 Jun 2017
Published online: 01 Oct 2018 *

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