Araştırma Makalesi
BibTex RIS Kaynak Göster

Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm

Yıl 2019, Cilt: 2 Sayı: 1, 7 - 12, 01.01.2019

Muhammed Kürşad Uçar

https://doi.org/10.38016/jista.498799
Cited By: 4

Öz

Feature selection algorithms are of great importance in the field of machine learning. Significant reduction of very large data is the main function of feature selection algorithms. These methods are still being developed today. The reason for this is that data structures are growing day by day. As the data increases, more advanced, better performance, feature selection algorithms are needed. In this study, Eta Correlation Coefficient based E-Score Feature selection algorithm was developed. Two versions were prepared for E-Score. We tested the performance of the E-Score method with three classifiers and compared with conventional F-Score Feature Selection Algorithm. According to the results, both versions of the E-Score feature selection algorithm have improved performance and is better than the F-Score. According to these results, it is thought that the E-Score Feature Selection Algorithm can be used in the field of machine learning.

Anahtar Kelimeler

Eta Correlation Coefficient E-Score Feature Selection Algorithm Feature Selection Methods

Kaynakça

  • [1] D. Guan, W. Yuan, Y.-K. Lee, K. Najeebullah, and M. K. Rasel, “A Review of Ensemble Learning Based Feature Selection,” IETE Tech. Rev., vol. 31, no. 3, pp. 190–198, 2014.
  • [2] K. Polat and S. Güneş, “A new feature selection method on classification of medical datasets: Kernel F-score feature selection,” Expert Syst. Appl., vol. 36, no. 7, pp. 10367–10373, Sep. 2009.
  • [3] A. R. Kavsaoğlu, K. Polat, and M. R. Bozkurt, “A novel feature ranking algorithm for biometric recognition with PPG signals.,” Comput. Biol. Med., vol. 49, pp. 1–14, Jun. 2014.
  • [4] J. Cai, J. Luo, S. Wang, and S. Yang, “Feature selection in machine learning: A new perspective,” Neurocomputing, vol. 300, pp. 70–79, Jul. 2018.
  • [5] T. Khoshgoftaar, D. Dittman, R. Wald, and A. Fazelpour, “First Order Statistics Based Feature Selection: A Diverse and Powerful Family of Feature Seleciton Techniques,” in 2012 11th International Conference on Machine Learning and Applications, 2012, pp. 151–157.
  • [6] A. Goltsev and V. Gritsenko, “Investigation of efficient features for image recognition by neural networks,” Neural Networks, vol. 28, pp. 15–23, Apr. 2012.
  • [7] H. Elghazel and A. Aussem, “Unsupervised feature selection with ensemble learning,” Mach. Learn., vol. 98, no. 1–2, pp. 157–180, Jan. 2015.
  • [8] Y. Li, S.-Y. Gao, and S. Chen, “Ensemble Feature Weighting Based on Local Learning and Diversity,” AAAI, 2012.
  • [9] D.-S. Huang, “Radial Basis Probabilistic Neural Networks: Model and Application,” Int. J. Pattern Recognit. Artif. Intell., vol. 13, no. 07, pp. 1083–1101, Nov. 1999.
  • [10] Tsang-Hsiang Cheng, Chih-Ping Wei, and V. S. Tseng, “Feature Selection for Medical Data Mining: Comparisons of Expert Judgment and Automatic Approaches,” in 19th IEEE Symposium on Computer-Based Medical Systems (CBMS’06), 2006, pp. 165–170.
  • [11] E. C. Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, “UCI Machine Learning Repository: Epileptic Seizure Recognition Data Set,” UCI, 2001. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition. [Accessed: 14-Aug-2018].
  • [12] R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” Phys. Rev. E - Stat. Physics, Plasmas, Fluids, Relat. Interdiscip. Top., vol. 64, no. 6, p. 8, Nov. 2001.
  • [13] R. Alpar, Applied Statistic and Validation - Reliability. Detay Publishing, 2010.
  • [14] R. Koch, The 80/20 Principle and 92 Other Powerful Laws of Nature: The Science of Success. 2014.

Yıl 2019, Cilt: 2 Sayı: 1, 7 - 12, 01.01.2019

Muhammed Kürşad Uçar

https://doi.org/10.38016/jista.498799
Cited By: 4

Öz

Kaynakça

  • [1] D. Guan, W. Yuan, Y.-K. Lee, K. Najeebullah, and M. K. Rasel, “A Review of Ensemble Learning Based Feature Selection,” IETE Tech. Rev., vol. 31, no. 3, pp. 190–198, 2014.
  • [2] K. Polat and S. Güneş, “A new feature selection method on classification of medical datasets: Kernel F-score feature selection,” Expert Syst. Appl., vol. 36, no. 7, pp. 10367–10373, Sep. 2009.
  • [3] A. R. Kavsaoğlu, K. Polat, and M. R. Bozkurt, “A novel feature ranking algorithm for biometric recognition with PPG signals.,” Comput. Biol. Med., vol. 49, pp. 1–14, Jun. 2014.
  • [4] J. Cai, J. Luo, S. Wang, and S. Yang, “Feature selection in machine learning: A new perspective,” Neurocomputing, vol. 300, pp. 70–79, Jul. 2018.
  • [5] T. Khoshgoftaar, D. Dittman, R. Wald, and A. Fazelpour, “First Order Statistics Based Feature Selection: A Diverse and Powerful Family of Feature Seleciton Techniques,” in 2012 11th International Conference on Machine Learning and Applications, 2012, pp. 151–157.
  • [6] A. Goltsev and V. Gritsenko, “Investigation of efficient features for image recognition by neural networks,” Neural Networks, vol. 28, pp. 15–23, Apr. 2012.
  • [7] H. Elghazel and A. Aussem, “Unsupervised feature selection with ensemble learning,” Mach. Learn., vol. 98, no. 1–2, pp. 157–180, Jan. 2015.
  • [8] Y. Li, S.-Y. Gao, and S. Chen, “Ensemble Feature Weighting Based on Local Learning and Diversity,” AAAI, 2012.
  • [9] D.-S. Huang, “Radial Basis Probabilistic Neural Networks: Model and Application,” Int. J. Pattern Recognit. Artif. Intell., vol. 13, no. 07, pp. 1083–1101, Nov. 1999.
  • [10] Tsang-Hsiang Cheng, Chih-Ping Wei, and V. S. Tseng, “Feature Selection for Medical Data Mining: Comparisons of Expert Judgment and Automatic Approaches,” in 19th IEEE Symposium on Computer-Based Medical Systems (CBMS’06), 2006, pp. 165–170.
  • [11] E. C. Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, “UCI Machine Learning Repository: Epileptic Seizure Recognition Data Set,” UCI, 2001. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition. [Accessed: 14-Aug-2018].
  • [12] R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” Phys. Rev. E - Stat. Physics, Plasmas, Fluids, Relat. Interdiscip. Top., vol. 64, no. 6, p. 8, Nov. 2001.
  • [13] R. Alpar, Applied Statistic and Validation - Reliability. Detay Publishing, 2010.
  • [14] R. Koch, The 80/20 Principle and 92 Other Powerful Laws of Nature: The Science of Success. 2014.
Toplam 14 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Muhammed Kürşad Uçar

Yayımlanma Tarihi 1 Ocak 2019
Gönderilme Tarihi 18 Aralık 2018
Yayımlandığı Sayı Yıl 2019 Cilt: 2 Sayı: 1

Kaynak Göster

APA Uçar, M. K. (2019). Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm. Journal of Intelligent Systems: Theory and Applications, 2(1), 7-12. https://doi.org/10.38016/jista.498799
AMA Uçar MK. Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm. jista. Ocak 2019;2(1):7-12. doi:10.38016/jista.498799
Chicago Uçar, Muhammed Kürşad. “Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm”. Journal of Intelligent Systems: Theory and Applications 2, sy. 1 (Ocak 2019): 7-12. https://doi.org/10.38016/jista.498799.
EndNote Uçar MK (01 Ocak 2019) Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm. Journal of Intelligent Systems: Theory and Applications 2 1 7–12.
IEEE M. K. Uçar, “Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm”, jista, c. 2, sy. 1, ss. 7–12, 2019, doi: 10.38016/jista.498799.
ISNAD Uçar, Muhammed Kürşad. “Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm”. Journal of Intelligent Systems: Theory and Applications 2/1 (Ocak 2019), 7-12. https://doi.org/10.38016/jista.498799.
JAMA Uçar MK. Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm. jista. 2019;2:7–12.
MLA Uçar, Muhammed Kürşad. “Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm”. Journal of Intelligent Systems: Theory and Applications, c. 2, sy. 1, 2019, ss. 7-12, doi:10.38016/jista.498799.
Vancouver Uçar MK. Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm. jista. 2019;2(1):7-12.

Cited By

A Novel Machine Learning-based Diagnostic Algorithm for Detection of Onychomycosis through Nail Appearance
Sakarya University Journal of Science
https://doi.org/10.16984/saufenbilder.1216668
EOG İşaretini Kaynak İşaret Olarak Kullanmak Üzere Etkin Özelliklerin Belirlenmesi
Sakarya University Journal of Computer and Information Sciences
https://doi.org/10.35377/saucis.02.03.649002
Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection
Computational and Mathematical Methods in Medicine
https://doi.org/10.1155/2022/5714454
Tarımda Kaliteli Tohum Üretimi için Kuru Fasulye Türlerinin Yapay Zekâ Tabanlı Sınıflandırılması
El-Cezeri Fen ve Mühendislik Dergisi
https://doi.org/10.31202/ecjse.1135807
 Kapak Resmi İndir

Zeki Sistemler Teori ve Uygulamaları Dergisi