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- P. Kraipeerapun, Chun Che Fung
- 2008 2nd IEEE International Conference on Digital…
- 2008

In this paper, the classification results obtained from several kinds of support vector machines (SVM) and neural networks (NN) are compared with our proposed classifier. Our approach is based on neural networks and interval neutrosophic sets which are used to classify the input patterns into one of the two binary class outputs. The comparison is based on… (More)

- P. Kraipeerapun, Chun Che Fung, W. Brown, Kok-Wai Wong
- 2006 IEEE Conference on Cybernetics and…
- 2006

This paper describes the integration of neural network ensembles and interval neutrosophic sets using bagging technique for predicting regional-scale potential for mineral deposits as well as quantifying uncertainty in the predictions. Uncertainty in the types of error and vagueness are considered in this paper. Each component in the ensemble consists of a… (More)

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… (More)

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… (More)

— This paper proposes a novel approach to the question of lithofacies classification based on an assessment of the uncertainty in the classification results. The proposed approach has multiple neural networks (NN), and interval neutrosophic sets (INS) are used to classify the input well log data into outputs of multiple classes of lithofacies. A pair of… (More)

In this paper, both truth and falsity inputs are used to trained neural networks. Falsity input is the complement of the truth input. Two pairs of neural networks are created. The first pair of neural networks are trained using the truth input whereas the second pair of neural networks are trained using the falsity input. Each pair of neural networks are… (More)

- JAIRAJ PROMRAK, PAWALAI KRAIPEERAPUN
- 2011

This paper presented an innovative method, combining Complementary Neural Networks (CMTNN) and Error-Correcting Output Codes (ECOC), to solve multiclass classification problem. CMTNN consist of truth neural network and falsity neural network created based on truth and falsity information, respectively. In the experiment, we deal with feed-forward… (More)

This paper proposes an approach to solve binary classification problems using Duo Output Neural Network (DONN). DONN is a neural network trained to predict a pair of complementary outputs which are the truth and falsity values. In this paper, outputs obtained from two DONNs are aggregated and used to predict the classification result. The first DONN is… (More)

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