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Open AccessFeature PaperArticle

A Novel Neutrosophic Weighted Extreme Learning Machine for Imbalanced Data Set

1
Department of Electrical and Electronics Engineering, Technology Faculty, Firat University, 23119 Elazig, Turkey
2
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.
Academic Editor: Florentin Smarandache
Symmetry 2017, 9(8), 142; https://doi.org/10.3390/sym9080142
Received: 23 June 2017 / Revised: 31 July 2017 / Accepted: 1 August 2017 / Published: 3 August 2017
(This article belongs to the Special Issue Neutrosophic Theories Applied in Engineering)
Extreme learning machine (ELM) is known as a kind of single-hidden layer feedforward network (SLFN), and has obtained considerable attention within the machine learning community and achieved various real-world applications. It has advantages such as good generalization performance, fast learning speed, and low computational cost. However, the ELM might have problems in the classification of imbalanced data sets. In this paper, we present a novel weighted ELM scheme based on neutrosophic set theory, denoted as neutrosophic weighted extreme learning machine (NWELM), in which neutrosophic c-means (NCM) clustering algorithm is used for the approximation of the output weights of the ELM. We also investigate and compare NWELM with several weighted algorithms. The proposed method demonstrates advantages to compare with the previous studies on benchmarks. View Full-Text
Keywords: extreme learning machine (ELM); weight; neutrosophic c-means (NCM); imbalanced data set extreme learning machine (ELM); weight; neutrosophic c-means (NCM); imbalanced data set
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MDPI and ACS Style

Akbulut, Y.; Şengür, A.; Guo, Y.; Smarandache, F. A Novel Neutrosophic Weighted Extreme Learning Machine for Imbalanced Data Set. Symmetry 2017, 9, 142.

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