NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier
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Department of Electrical and Electronics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey
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Department of Computer Science, University of Illinois at Springfield, Springfield, IL 62703, USA
3
Department of Mathematics and Sciences, University of New Mexico, Gallup, NM 87301, USA
*
Author to whom correspondence should be addressed.
Symmetry 2017, 9(9), 179; https://doi.org/10.3390/sym9090179
Received: 2 August 2017
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Revised: 16 August 2017
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Accepted: 29 August 2017
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Published: 2 September 2017
(This article belongs to the Special Issue Neutrosophic Theories Applied in Engineering)
k-nearest neighbors (k-NN), which is known to be a simple and efficient approach, is a non-parametric supervised classifier. It aims to determine the class label of an unknown sample by its k-nearest neighbors that are stored in a training set. The k-nearest neighbors are determined based on some distance functions. Although k-NN produces successful results, there have been some extensions for improving its precision. The neutrosophic set (NS) defines three memberships namely T, I and F. T, I, and F shows the truth membership degree, the false membership degree, and the indeterminacy membership degree, respectively. In this paper, the NS memberships are adopted to improve the classification performance of the k-NN classifier. A new straightforward k-NN approach is proposed based on NS theory. It calculates the NS memberships based on a supervised neutrosophic c-means (NCM) algorithm. A final belonging membership U is calculated from the NS triples as . A similar final voting scheme as given in fuzzy k-NN is considered for class label determination. Extensive experiments are conducted to evaluate the proposed method’s performance. To this end, several toy and real-world datasets are used. We further compare the proposed method with k-NN, fuzzy k-NN, and two weighted k-NN schemes. The results are encouraging and the improvement is obvious.
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Keywords:
k-NN; Fuzzy k-NN; neutrosophic sets; data classification
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MDPI and ACS Style
Akbulut, Y.; Sengur, A.; Guo, Y.; Smarandache, F. NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier. Symmetry 2017, 9, 179. https://doi.org/10.3390/sym9090179
AMA Style
Akbulut Y, Sengur A, Guo Y, Smarandache F. NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier. Symmetry. 2017; 9(9):179. https://doi.org/10.3390/sym9090179
Chicago/Turabian StyleAkbulut, Yaman, Abdulkadir Sengur, Yanhui Guo, and Florentin Smarandache. 2017. "NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier" Symmetry 9, no. 9: 179. https://doi.org/10.3390/sym9090179
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