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A classification method based on Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices

Yıl 2021, Cilt: 10 Sayı: 2, 59 - 76, 31.08.2021

Öz

In this study, firstly, Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices) have been defined. Afterwards, a classifier based on Hamming pseudo-similarity of ifpifs-matrices (IFPIFS-HC) has been developed. The classifier's simulations have been performed using datasets provided in the UCI Machine Learning Database, and its performance results via the performance metrics accuracy, precision, recall, macro F-score, and micro F-score have been obtained. Thereafter, the results have been compared with those of the well-known methods. Then, the statistical evaluations of the performance results have been conducted using Friedman and Nemenyi post-hoc tests, and the critical diagrams of the Nemenyi post-hoc test are presented. The results and the statistical evaluations show that the proposed classifier has performed better than the others in 12 of 21 datasets in terms of the five performance metrics, in 4 of 21 in terms of the four performance metrics, and 17 of 21 in terms of accuracy performance metric. Moreover, the mean accuracy, precision, recall, precision, macro F-score, and micro F-score results of Fuzzy kNN, FSSC, FussCyier, HDFSSC, and FPFS-EC for the 21 datasets are 84.90, 71.96, 67.95, 71.91, and 75.28; 78.12, 68.01, 68.05, 66.53, and 67.68; 80.76, 68.63, 69.07, 68.36, and 70.65; 81.93, 69.43, 69.95, 70.25, and 72.36; and 89.59, 80.27, 78.40, 81.20, and 83.60, while those of IFPIFS-HC are 90.59, 82.88, 80.75, 82.89, and 85.48, respectively. Finally, the applications of ifpifs-matrices to machine learning have been discussed for further research.

Destekleyen Kurum

Çanakkale Onsekiz Mart University

Proje Numarası

FHD-2020-3465

Teşekkür

This work was supported by the Office of Scientific Research Projects Coordination at Çanakkale Onsekiz Mart University, Grant number: FHD-2020-3465.

Kaynakça

  • L. A. Zadeh, Fuzzy sets, Information and Control, 8(3), (1965) 338–353.
  • D. Molodtsov, Soft set theory-first results, Computers and Mathematics with Applications, 37(4–5), (1999) 19–31.
  • P. K. Maji, R. Biswas, A. R. Roy, Fuzzy soft sets, The Journal of Fuzzy Mathematics, 9(3), (2001) 589–602.
  • N. Çağman, F. Çıtak, S. Enginoğlu, FP-soft set theory and its applications, Annals of Fuzzy Mathematics and Informatics, 2(2), (2011) 219–226.
  • N. Çağman, S. Enginoğlu, F. Çıtak, Fuzzy soft set theory and its applications, Iranian Journal of Fuzzy Systems, 8(3), (2011) 137–147.
  • N. Çağman, F. Çıtak, S. Enginoğlu, Fuzzy parameterized fuzzy soft set theory and its applications, Turkish Journal of Fuzzy Systems, 1(1) (2010) 21–35.
  • S. Enginoğlu, N. Çağman, Fuzzy parameterized fuzzy soft matrices and their application in decision-making, TWMS Journal of Applied and Engineering Mathematics, 10(4), (2020) 1105–1115.
  • S. Enginoğlu, S. Memiş, A configuration of some soft decision-making algorithms via fpfs-matrices, Cumhuriyet Science Journal, 39(4), (2018) 871–881.
  • T. Aydın, S. Enginoğlu, A configuration of five of the soft decision-making methods via fuzzy parameterized fuzzy soft matrices and their application to a performance-based value assignment problem, in: M. Kılıç, K. Özkan, M. Karaboyacı, K. Taşdelen, H. Kandemir, A. Beram (Eds.), International Conferences on Natural Sciences and Technology, Prizren, KOSOVO, 2019, pp. 56–67.
  • S. Enginoğlu, T. Öngel, Configurations of several soft decision-making methods to operate in fpfs-matrices space, Eskişehir Technical University Journal of Science and Technology A-Applied Sciences and Engineering, 21(1), (2020) 58–71.
  • S. Enginoğlu, T. Aydın, S. Memiş, B. Arslan, Operability-oriented configurations of the soft decision-making methods proposed between 2013 and 2016 and their comparisons, Journal of New Theory, (34), (2021) 82–114.
  • S. Enginoğlu, T. Aydın, S. Memiş, B. Arslan, SDM methods’ configurations (2017-2019) and their application to a performance-based value assignment problem: A follow up study, Annals of Optimization Theory and Practice, 4(1), (2021) 41-85.
  • S. Enginoğlu, S. Memiş, Comment on “Fuzzy soft sets” [The Journal of Fuzzy Mathematics 9(3), 2001, 14 589-602], International Journal of Latest Engineering Research and Applications, 3(9), (2018) 1–9.
  • S. Enginoğlu, S. Memiş, T. Öngel, Comment on soft set theory and uni-int decision-making [E European Journal of Operational Research, (2010) 207, 848-855], Journal of New Results in Science, 7(3), (2018) 28–43.
  • S. Enginoğlu, S. Memiş, B. Arslan, Comment (2) on soft set theory and uni-int decision-making [European Journal of Operational Research, (2010) 207, 848-855], Journal of New Theory, (25), (2018) 84–102.
  • S. Enginoğlu, S. Memiş, N. Çağman, A generalisation of fuzzy soft max-min decision-making method and its application to a performance-based value assignment in image denoising. El-Cezerî Journal of Science and Engineering, 6(3), (2019) 466–481.
  • S. Enginoğlu, S. Memiş, F. Karaaslan, A new approach to group decision-making method based on TOPSIS under fuzzy soft environment, Journal of New Results in Science, 8(2), (2019) 42–52.
  • S. Enginoğlu, S. Memiş, A new approach to the criteria-weighted fuzzy soft max-min decision-making method and its application to a performance-based value assignment problem, Journal of New Results in Science, 9(1), (2020) 19–36.
  • S. Memiş, S. Enginoğlu, U. Erkan, A data classification method in machine learning based on normalised hamming pseudo-similarity of fuzzy parameterized fuzzy soft matrices, Bilge International Journal of Science and Technology Research, 3(Special Issue), (2019) 1–8.
  • S. Memiş, S. Enginoğlu, An application of fuzzy parameterized fuzzy soft matrices in data classification, in: M. Kılıç, K. Özkan, M. Karaboyacı, K. Taşdelen, H. Kandemir, A. Beram (Eds.), International Conferences on Natural Sciences and Technology, Prizren, KOSOVO, 2019, pp. 68–77.
  • K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20, (1986) 87–96.
  • P. K. Maji, R. Biswas, A. R. Roy, Intuitionistic fuzzy soft sets, The Journal of Fuzzy Mathematics, 9(3), (2001): 677–692.
  • İ. Deli, N. Çağman, Intuitionistic fuzzy parameterized soft set theory and its decision making. Applied Soft Computing, 28, (2015) 109–113.
  • E. El-Yagubi, A. R. Salleh, Intuitionistic fuzzy parameterised fuzzy soft set, Journal of Quality Measurement and Analysis, 9(2), (2013) 73–81.
  • E. Sulukan, N. Çağman, T. Aydın, Fuzzy parameterized intuitionistic fuzzy soft sets and their application to a performance-based value assignment problem, Journal of New Theory, (29), (2019) 79–88.
  • F. Karaaslan, Intuitionistic fuzzy parameterized intuitionistic fuzzy soft sets with applications in decision making, Annals of Fuzzy Mathematics and Informatics 11(4), (2016) 607–619.
  • S. Enginoğlu, B. Arslan, Intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices and their application in decision-making, Computational and Applied Mathematics, 39, (2020) Article number: 325.
  • B. Arslan, S. Enginoğlu, Algebraic operations of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices and their application to a performance-based value assignment problem, in: A. Özkan, S. Biroğul, Ö. Güngör, M. Of, Ç. Taşdemirci (Eds.), International Marmara Sciences Congress IMASCON 2020 – Autumn, Kocaeli, Turkey, 2020, pp. 127–137.
  • D. Dua, C. Graff, 2019. UCI Machine Learning Repository [Database].
  • J. M. Keller, M. R. Gray, J. A. Givens, A fuzzy k-nearest neighbor algorithm, IEEE Transactions on Systems, Man, and Cybernetics, 15, (1985) 580–585.
  • B. Handaga, H. Onn, T. Herawan, FSSC: An algorithm for classifying numerical data using fuzzy soft set theory, International Journal of Fuzzy System Applications, 3(4), (2012) 29–46.
  • S. A. Lashari, R. Ibrahim, N. Senan, Medical data classification using similarity measure of fuzzy soft set based distance measure, Journal of Telecommunication, Electronic and Computer Engineering, 9(2-9), (2017) 95–99.
  • I. T. R. Yanto, R. R. Seadudin, S. A. Lashari, Haviluddin, A numerical classification technique based on fuzzy soft set using hamming distance, in: R. Ghazali, M. M. Deris, N. M. Nawi, J. H. Abawajy (Eds.), Third International Conference on Soft Computing and Data Mining, Johor, Malaysia, 2018, pp. 252–260.
  • S. Memiş, S. Enginoğlu, U. Erkan, Numerical data classification via distance-based similarity measures of fuzzy parameterized fuzzy soft matrices, IEEE Access, 9, (2021) 88583–88601. M. Friedman, A comparison of alternative tests of significance for the problem of m rankings, Annals of Mathematical Statistics, 11(1), (1940): 86–92.
  • P. B. Nemenyi, Distribution-Free Multiple Comparisons. Princeton University, New Jersey, 1963.
  • T. Fawcett, An introduction to ROC analysis. Pattern Recognition Letters, 27, (2006) 861–874.
  • T. T. Nguyen, M. T. Dang, A. V. Luong, A. W. C. Liew, T. Liang, J. McCall, Multi-label classification via incremental clustering on an evolving data stream. Pattern Recognition, 95, (2019) 96–113.
  • M. Stone, Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, Series B (Methodological). 36, (1974) 111–147.
  • D. Üstün, A. Toktaş, A. Akdağlı, Deep neural network–based soft computing the resonant frequency of E–shaped patch antennas. International Journal of Electronics and Communications, 102, (2019) 54–61.
  • J. Demšar, Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, (2006) 1–30.
  • J. H. Zar, Biostatistical Analysis, Pearson Prentice Hall, Upper Saddle River, 2010, Volume 5, p. 672.
  • C. Cortes, V. Vapnik, Support-vector networks. Machine Learning 20(3), (1995) 273–297.
  • T. Aydın, S. Enginoğlu, Interval-valued intuitionistic fuzzy parameterized interval-valued intuitionistic fuzzy soft sets and their application in decision-making. Journal of Ambient Intelligence and Humanized Computing, 12, (2021) 1541–1558.
  • B. C. Cuong, Picture fuzzy sets, Journal of Computer Science and Cybernetics, 30(4), (2014) 409–420.
  • S. Memiş, A study on picture fuzzy sets, in: G. Çuvalcıoğlu (Ed.), 7th IFS and Contemporary Mathematics Conference, Mersin, Turkey, 2021, pp. 125–132.
Yıl 2021, Cilt: 10 Sayı: 2, 59 - 76, 31.08.2021

Öz

Proje Numarası

FHD-2020-3465

Kaynakça

  • L. A. Zadeh, Fuzzy sets, Information and Control, 8(3), (1965) 338–353.
  • D. Molodtsov, Soft set theory-first results, Computers and Mathematics with Applications, 37(4–5), (1999) 19–31.
  • P. K. Maji, R. Biswas, A. R. Roy, Fuzzy soft sets, The Journal of Fuzzy Mathematics, 9(3), (2001) 589–602.
  • N. Çağman, F. Çıtak, S. Enginoğlu, FP-soft set theory and its applications, Annals of Fuzzy Mathematics and Informatics, 2(2), (2011) 219–226.
  • N. Çağman, S. Enginoğlu, F. Çıtak, Fuzzy soft set theory and its applications, Iranian Journal of Fuzzy Systems, 8(3), (2011) 137–147.
  • N. Çağman, F. Çıtak, S. Enginoğlu, Fuzzy parameterized fuzzy soft set theory and its applications, Turkish Journal of Fuzzy Systems, 1(1) (2010) 21–35.
  • S. Enginoğlu, N. Çağman, Fuzzy parameterized fuzzy soft matrices and their application in decision-making, TWMS Journal of Applied and Engineering Mathematics, 10(4), (2020) 1105–1115.
  • S. Enginoğlu, S. Memiş, A configuration of some soft decision-making algorithms via fpfs-matrices, Cumhuriyet Science Journal, 39(4), (2018) 871–881.
  • T. Aydın, S. Enginoğlu, A configuration of five of the soft decision-making methods via fuzzy parameterized fuzzy soft matrices and their application to a performance-based value assignment problem, in: M. Kılıç, K. Özkan, M. Karaboyacı, K. Taşdelen, H. Kandemir, A. Beram (Eds.), International Conferences on Natural Sciences and Technology, Prizren, KOSOVO, 2019, pp. 56–67.
  • S. Enginoğlu, T. Öngel, Configurations of several soft decision-making methods to operate in fpfs-matrices space, Eskişehir Technical University Journal of Science and Technology A-Applied Sciences and Engineering, 21(1), (2020) 58–71.
  • S. Enginoğlu, T. Aydın, S. Memiş, B. Arslan, Operability-oriented configurations of the soft decision-making methods proposed between 2013 and 2016 and their comparisons, Journal of New Theory, (34), (2021) 82–114.
  • S. Enginoğlu, T. Aydın, S. Memiş, B. Arslan, SDM methods’ configurations (2017-2019) and their application to a performance-based value assignment problem: A follow up study, Annals of Optimization Theory and Practice, 4(1), (2021) 41-85.
  • S. Enginoğlu, S. Memiş, Comment on “Fuzzy soft sets” [The Journal of Fuzzy Mathematics 9(3), 2001, 14 589-602], International Journal of Latest Engineering Research and Applications, 3(9), (2018) 1–9.
  • S. Enginoğlu, S. Memiş, T. Öngel, Comment on soft set theory and uni-int decision-making [E European Journal of Operational Research, (2010) 207, 848-855], Journal of New Results in Science, 7(3), (2018) 28–43.
  • S. Enginoğlu, S. Memiş, B. Arslan, Comment (2) on soft set theory and uni-int decision-making [European Journal of Operational Research, (2010) 207, 848-855], Journal of New Theory, (25), (2018) 84–102.
  • S. Enginoğlu, S. Memiş, N. Çağman, A generalisation of fuzzy soft max-min decision-making method and its application to a performance-based value assignment in image denoising. El-Cezerî Journal of Science and Engineering, 6(3), (2019) 466–481.
  • S. Enginoğlu, S. Memiş, F. Karaaslan, A new approach to group decision-making method based on TOPSIS under fuzzy soft environment, Journal of New Results in Science, 8(2), (2019) 42–52.
  • S. Enginoğlu, S. Memiş, A new approach to the criteria-weighted fuzzy soft max-min decision-making method and its application to a performance-based value assignment problem, Journal of New Results in Science, 9(1), (2020) 19–36.
  • S. Memiş, S. Enginoğlu, U. Erkan, A data classification method in machine learning based on normalised hamming pseudo-similarity of fuzzy parameterized fuzzy soft matrices, Bilge International Journal of Science and Technology Research, 3(Special Issue), (2019) 1–8.
  • S. Memiş, S. Enginoğlu, An application of fuzzy parameterized fuzzy soft matrices in data classification, in: M. Kılıç, K. Özkan, M. Karaboyacı, K. Taşdelen, H. Kandemir, A. Beram (Eds.), International Conferences on Natural Sciences and Technology, Prizren, KOSOVO, 2019, pp. 68–77.
  • K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20, (1986) 87–96.
  • P. K. Maji, R. Biswas, A. R. Roy, Intuitionistic fuzzy soft sets, The Journal of Fuzzy Mathematics, 9(3), (2001): 677–692.
  • İ. Deli, N. Çağman, Intuitionistic fuzzy parameterized soft set theory and its decision making. Applied Soft Computing, 28, (2015) 109–113.
  • E. El-Yagubi, A. R. Salleh, Intuitionistic fuzzy parameterised fuzzy soft set, Journal of Quality Measurement and Analysis, 9(2), (2013) 73–81.
  • E. Sulukan, N. Çağman, T. Aydın, Fuzzy parameterized intuitionistic fuzzy soft sets and their application to a performance-based value assignment problem, Journal of New Theory, (29), (2019) 79–88.
  • F. Karaaslan, Intuitionistic fuzzy parameterized intuitionistic fuzzy soft sets with applications in decision making, Annals of Fuzzy Mathematics and Informatics 11(4), (2016) 607–619.
  • S. Enginoğlu, B. Arslan, Intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices and their application in decision-making, Computational and Applied Mathematics, 39, (2020) Article number: 325.
  • B. Arslan, S. Enginoğlu, Algebraic operations of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices and their application to a performance-based value assignment problem, in: A. Özkan, S. Biroğul, Ö. Güngör, M. Of, Ç. Taşdemirci (Eds.), International Marmara Sciences Congress IMASCON 2020 – Autumn, Kocaeli, Turkey, 2020, pp. 127–137.
  • D. Dua, C. Graff, 2019. UCI Machine Learning Repository [Database].
  • J. M. Keller, M. R. Gray, J. A. Givens, A fuzzy k-nearest neighbor algorithm, IEEE Transactions on Systems, Man, and Cybernetics, 15, (1985) 580–585.
  • B. Handaga, H. Onn, T. Herawan, FSSC: An algorithm for classifying numerical data using fuzzy soft set theory, International Journal of Fuzzy System Applications, 3(4), (2012) 29–46.
  • S. A. Lashari, R. Ibrahim, N. Senan, Medical data classification using similarity measure of fuzzy soft set based distance measure, Journal of Telecommunication, Electronic and Computer Engineering, 9(2-9), (2017) 95–99.
  • I. T. R. Yanto, R. R. Seadudin, S. A. Lashari, Haviluddin, A numerical classification technique based on fuzzy soft set using hamming distance, in: R. Ghazali, M. M. Deris, N. M. Nawi, J. H. Abawajy (Eds.), Third International Conference on Soft Computing and Data Mining, Johor, Malaysia, 2018, pp. 252–260.
  • S. Memiş, S. Enginoğlu, U. Erkan, Numerical data classification via distance-based similarity measures of fuzzy parameterized fuzzy soft matrices, IEEE Access, 9, (2021) 88583–88601. M. Friedman, A comparison of alternative tests of significance for the problem of m rankings, Annals of Mathematical Statistics, 11(1), (1940): 86–92.
  • P. B. Nemenyi, Distribution-Free Multiple Comparisons. Princeton University, New Jersey, 1963.
  • T. Fawcett, An introduction to ROC analysis. Pattern Recognition Letters, 27, (2006) 861–874.
  • T. T. Nguyen, M. T. Dang, A. V. Luong, A. W. C. Liew, T. Liang, J. McCall, Multi-label classification via incremental clustering on an evolving data stream. Pattern Recognition, 95, (2019) 96–113.
  • M. Stone, Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, Series B (Methodological). 36, (1974) 111–147.
  • D. Üstün, A. Toktaş, A. Akdağlı, Deep neural network–based soft computing the resonant frequency of E–shaped patch antennas. International Journal of Electronics and Communications, 102, (2019) 54–61.
  • J. Demšar, Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, (2006) 1–30.
  • J. H. Zar, Biostatistical Analysis, Pearson Prentice Hall, Upper Saddle River, 2010, Volume 5, p. 672.
  • C. Cortes, V. Vapnik, Support-vector networks. Machine Learning 20(3), (1995) 273–297.
  • T. Aydın, S. Enginoğlu, Interval-valued intuitionistic fuzzy parameterized interval-valued intuitionistic fuzzy soft sets and their application in decision-making. Journal of Ambient Intelligence and Humanized Computing, 12, (2021) 1541–1558.
  • B. C. Cuong, Picture fuzzy sets, Journal of Computer Science and Cybernetics, 30(4), (2014) 409–420.
  • S. Memiş, A study on picture fuzzy sets, in: G. Çuvalcıoğlu (Ed.), 7th IFS and Contemporary Mathematics Conference, Mersin, Turkey, 2021, pp. 125–132.
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Matematik, Uygulamalı Matematik, Mühendislik
Bölüm Articles
Yazarlar

Samet Memiş 0000-0002-0958-5872

Burak Arslan 0000-0002-1724-8841

Tuğçe Aydın 0000-0002-8134-1004

Serdar Enginoğlu 0000-0002-7188-9893

Çetin Camcı 0000-0002-0122-559X

Proje Numarası FHD-2020-3465
Yayımlanma Tarihi 31 Ağustos 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 10 Sayı: 2

Kaynak Göster

APA Memiş, S., Arslan, B., Aydın, T., Enginoğlu, S., vd. (2021). A classification method based on Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices. Journal of New Results in Science, 10(2), 59-76.
AMA Memiş S, Arslan B, Aydın T, Enginoğlu S, Camcı Ç. A classification method based on Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices. JNRS. Ağustos 2021;10(2):59-76.
Chicago Memiş, Samet, Burak Arslan, Tuğçe Aydın, Serdar Enginoğlu, ve Çetin Camcı. “A Classification Method Based on Hamming Pseudo-Similarity of Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Matrices”. Journal of New Results in Science 10, sy. 2 (Ağustos 2021): 59-76.
EndNote Memiş S, Arslan B, Aydın T, Enginoğlu S, Camcı Ç (01 Ağustos 2021) A classification method based on Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices. Journal of New Results in Science 10 2 59–76.
IEEE S. Memiş, B. Arslan, T. Aydın, S. Enginoğlu, ve Ç. Camcı, “A classification method based on Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices”, JNRS, c. 10, sy. 2, ss. 59–76, 2021.
ISNAD Memiş, Samet vd. “A Classification Method Based on Hamming Pseudo-Similarity of Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Matrices”. Journal of New Results in Science 10/2 (Ağustos 2021), 59-76.
JAMA Memiş S, Arslan B, Aydın T, Enginoğlu S, Camcı Ç. A classification method based on Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices. JNRS. 2021;10:59–76.
MLA Memiş, Samet vd. “A Classification Method Based on Hamming Pseudo-Similarity of Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Matrices”. Journal of New Results in Science, c. 10, sy. 2, 2021, ss. 59-76.
Vancouver Memiş S, Arslan B, Aydın T, Enginoğlu S, Camcı Ç. A classification method based on Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices. JNRS. 2021;10(2):59-76.


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