Conference paper
Ensemble neural networks using interval neutrosophic sets and bagging
IEEE
3rd International Conference on Natural Computation (ICNC 2007) (Haikou, 24/08/2007–27/08/2007)
2007
Abstract
This paper presents an approach to the problem of binary classification using ensemble neural networks based on interval neutrosophic sets and bagging technique. Each component in the ensemble consists of a pair of neural networks trained to predict the degree of truth and false membership values. Uncertainties in the prediction are also estimated and represented using the indeterminacy membership values. These three membership values collectively form an interval neutrosophic set. In order to combine and classify outputs from components in the ensemble, the outputs of an ensemble are dynamically weighted and summed. The proposed approach has been tested with three benchmarking UCI data sets, which are ionosphere, pima, and liver. The proposed ensemble method improves the classification performance as compared to the simple majority vote and averaging methods which were applied only to the truth membership value. Furthermore, the results obtained from the proposed ensemble method also outperform the results obtained from a single pair of networks and the results obtained from a single truth network.
Details
- Title
- Ensemble neural networks using interval neutrosophic sets and bagging
- Authors/Creators
- P. Kraipeerapun (Author/Creator)C.C. Fung (Author/Creator)K.W. Wong (Author/Creator)
- Conference
- 3rd International Conference on Natural Computation (ICNC 2007) (Haikou, 24/08/2007–27/08/2007)
- Publisher
- IEEE
- Identifiers
- 991005543302407891
- Copyright
- © 2007 IEEE
- Murdoch Affiliation
- School of Information Technology
- Language
- English
- Resource Type
- Conference paper
- Note
- Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Metrics
279 File views/ downloads
78 Record Views