Multiclass classification using neural networks and interval neutrosophic sets

Authors:
Pawalai Kraipeerapun;Chun Che Fung;Kok Wai Wong
Affiliations:
School of Information Technology, Murdoch University, Perth, Western Australia, Australia;School of Information Technology, Murdoch University, Perth, Western Australia, Australia;School of Information Technology, Murdoch University, Perth, Western Australia, Australia
Venue:
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Year:
2006

Citing 11
Cited 0

Bagging predictors

Machine Learning
The constraint based decomposition (CBD) training architecture

Neural Networks
On the Learnability and Design of Output Codes for Multiclass Problems

Machine Learning
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributed Output Encoding for Multi-Class Pattern Recognition

ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Multiclass Pattern Classification Using Neural Networks

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Boosting Neural Networks

Neural Computation
Mineral prospectivity prediction using interval neutrosophic sets

AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Solving multiclass learning problems via error-correcting output codes

Journal of Artificial Intelligence Research
Constructing diverse classifier ensembles using artificial training examples

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Assessment of uncertainty in mineral prospectivity prediction using interval neutrosophic set

CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new approach to the problem of multiclass classification. The proposed approach has the capability to provide an assessment of the uncertainty value associated with the results of the prediction. Two feed-forward backpropagation neural networks, each with multiple outputs, are used. One network is used to predict degrees of truth membership and another network is used to predict degrees of false membership. Indeterminacy membership or uncertainty in the prediction of these two memberships is also estimated. Together these three membership values form an interval neutrosophic set. Hence, a pair of single multiclass neural networks with multiple outputs produces multiple interval neutrosophic sets. We experiment our technique to the classical benchmark problems including balance, ecoli, glass, lenses, wine, yeast, and zoo from the UCI machine learning repository. Our approach improves classification performance compared to an existing technique which applied only to the truth membership created from a single neural network with multiple outputs.