Assessment of Uncertainty in Mineral Prospectivity Prediction Using Interval Neutrosophic Set

Kraipeerapun, Pawalai; Fung, Chun Che; Brown, Warick

doi:10.1007/11596981_160

Cited by 9 publications

(10 citation statements)

References 4 publications

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“…The average results are shown in the fourth row. In the fifth row, the technique presented in our previous paper [5] is applied. A single pair of neural networks is trained.…”

Section: Experimental Methodology and Resultsmentioning

confidence: 99%

“…We found that interval neutrosophic sets can represent uncertainty information and support the classification quite well. In this paper, we extend the work from our previous paper [5] by applying ensemble neural networks, interval neutrosophic sets, and a bagging technique to the problem of binary classification. Figure 1 shows the proposed training model that applies interval neutrosophic sets and a bagging technique to the ensemble neural network.…”

Section: Binary Classification Using Interval Neutrosophic Sets Ensementioning

confidence: 99%

“…In our previous papers [5], we integrated neural networks with interval neutrosophic sets in order to classify mineral prospectivity into deposit or barren cell. A pair of neural networks was created to predict degree of truth and false membership values.…”

Section: Binary Classification Using Interval Neutrosophic Sets Ensementioning

confidence: 99%

See 2 more Smart Citations

Ensemble Neural Networks Using Interval Neutrosophic Sets and Bagging

Kraipeerapun

Fung

Wong

2007

Third International Conference on Natural Computation (ICNC 2007)

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“…The average results are shown in the fourth row. In the fifth row, the technique presented in our previous paper [5] is applied. A single pair of neural networks is trained.…”

Section: Experimental Methodology and Resultsmentioning

confidence: 99%

Section: Binary Classification Using Interval Neutrosophic Sets Ensementioning

confidence: 99%

See 1 more Smart Citation

Ensemble Neural Networks Using Interval Neutrosophic Sets and Bagging

Kraipeerapun

Fung

Wong

2007

Third International Conference on Natural Computation (ICNC 2007)

Self Cite

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show abstract

“…In our previous papers [26][27][28][29], we combined neural networks with interval neutrosophic sets in order to classify prediction of mineral prospectivity from a set of data into deposit or barren cells. We found that an interval neutrosophic set can represent uncertainty information and supports the classification quite well.…”

Section: Interval Neutrosophic Setsmentioning

confidence: 99%

Binary classification using ensemble neural networks and interval neutrosophic sets

Kraipeerapun

Fung

2009

Neurocomputing

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This paper presents an ensemble neural network and interval neutrosophic sets approach to the problem of binary classification. A bagging technique is applied to an ensemble of pairs of neural networks created to predict degree of truth membership, indeterminacy membership, and false membership values in the interval neutrosophic sets. In our approach, the error and vagueness are quantified in the classification process as well. A number of aggregation techniques are proposed in this paper. We applied our techniques to the classical benchmark problems including ionosphere, pimaIndians diabetes, and liver-disorders from the UCI machine learning repository. Our approaches improve the classification performance as compared to the existing techniques which applied only to the truth membership values. Furthermore, the proposed ensemble techniques also provide better results than those obtained from only a single pair of neural networks.

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“…Several researchers applied neutrosophic sets effectively for image segmentation problems [4][5][6][7][8][9]. Neutrosophic sets are also applied for integrating geographic information system data [10] and for binary classification problems [11].…”

Section: Introductionmentioning

confidence: 99%

NC-Cross Entropy Based MADM Strategy in Neutrosophic Cubic Set Environment

Pramanik¹,

Dalapati

Alam

et al. 2018

Mathematics

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The objective of the paper is to introduce a new cross entropy measure in a neutrosophic cubic set (NCS) environment, which we call NC-cross entropy measure. We prove its basic properties. We also propose weighted NC-cross entropy and investigate its basic properties. We develop a novel multi attribute decision-making (MADM) strategy based on a weighted NC-cross entropy measure. To show the feasibility and applicability of the proposed multi attribute decision-making strategy, we solve an illustrative example of the multi attribute decision-making problem.

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Assessment of Uncertainty in Mineral Prospectivity Prediction Using Interval Neutrosophic Set

Cited by 9 publications

References 4 publications

Ensemble Neural Networks Using Interval Neutrosophic Sets and Bagging

Ensemble Neural Networks Using Interval Neutrosophic Sets and Bagging

Binary classification using ensemble neural networks and interval neutrosophic sets

NC-Cross Entropy Based MADM Strategy in Neutrosophic Cubic Set Environment

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