Have a personal or library account? Click to login
By:
Open Access
|Dec 2021

References

  1. Abdel-Hamid, O., Mohamed, A.-R., Jiang, H., Deng, L., Penn, G. and Yu, D. (2014a). Convolutional neural networks for speech recognition, IEEE/ACM Transactions on Audio, Speech, and Language Processing 22(10): 1533–1545.10.1109/TASLP.2014.2339736
  2. Abdel-Hamid, O., Mohamed, A.-R., Jiang, H., Deng, L., Penn, G. and Yu, D. (2014b). Convolutional neural networks for speech recognition, IEEE/ACM Transactions on Audio, Speech, and Language Processing 22(10): 1533–1545.10.1109/TASLP.2014.2339736
  3. Abdeljaber, O., Avci, O., Kiranyaz, M.S., Boashash, B., Sodano, H. and Inman, D.J. (2018). 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data, Neurocomputing 275: 1308–1317.10.1016/j.neucom.2017.09.069
  4. Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M. and Inman, D.J. (2017). Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks, Journal of Sound and Vibration 388: 154–170.10.1016/j.jsv.2016.10.043
  5. Awada, W., Khoshgoftaar, T.M., Dittman, D., Wald, R. and Napolitano, A. (2012). A review of the stability of feature selection techniques for bioinformatics data, IEEE 13th International Conference on Information Reuse & Integration (IRI), Las Vegas, USA, pp. 356–363.
  6. Azizjon, M., Jumabek, A. and Kim, W. (2020). 1D CNN based network intrusion detection with normalization on imbalanced data, International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan, pp. 218–224.
  7. Benesty, J., Chen, J., Huang, Y. and Cohen, I. (2009). Pearson correlation coefficient, in J. Benesty and W. Kellermann (Eds.), Noise Reduction in Speech Processing, Springer Topics in Signal Processing, Springer, Berlin, pp. 1–4.10.1007/978-3-642-00296-0_5
  8. Bolón-Canedo, V., Sánchez-Maroño, N. and Alonso-Betanzos, A. (2013). A review of feature selection methods on synthetic data, Knowledge and Information Systems 34(3): 483–519.10.1007/s10115-012-0487-8
  9. Breiman, L., Friedman, J., Olshen, R. and Stone, C. (1984). Classification and Regression Trees, CRC Press, Boca Raton.
  10. Broughton, R., Coope, I., Renaud, P. and Tappenden, R. (2010). Determinant and exchange algorithms for observation subset selection, IEEE Transactions on Image Processing 19(9): 2437–2443.10.1109/TIP.2010.204815020388597
  11. Cannas, L.M., Dessì, N. and Pes, B. (2013). Assessing similarity of feature selection techniques in high-dimensional domains, Pattern Recognition Letters 34(12): 1446–1453.10.1016/j.patrec.2013.05.011
  12. Devijver, P. and Kittler, I. (1982). Pattern Recognition: A Statistical Approach, Prentice-Hall, Englewood Cliffs.
  13. Dua, D. and Graff, C. (2017). UCI Machine Learning Repository, http://archive.ics.uci.edu/ml.
  14. El Aboudi, N. and Benhlima, L. (2016). Review on wrapper feature selection approaches, International Conference on Engineering & MIS (ICEMIS), Agadir, Morocco, pp. 1–5.
  15. Eren, L. (2017). Bearing fault detection by one-dimensional convolutional neural networks, Mathematical Problems in Engineering 2017: 1–9.10.1155/2017/8617315
  16. Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection, Journal of Machine Learning Research 3: 1157–1182.
  17. Hajj, N., Rizk, Y. and Awad, M. (2019). A subjectivity classification framework for sports articles using cortical algorithms for feature selection, Neural Computing and Applications 31: 8069–8085.10.1007/s00521-018-3549-3
  18. Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M. and Inman, D.J. (2021). 1D convolutional neural networks and applications: A survey, Mechanical Systems and Signal Processing 151: 107398.10.1016/j.ymssp.2020.107398
  19. Kiranyaz, S., Ince, T. and Gabbouj, M. (2015a). Real-time patient-specific ECG classification by 1-D convolutional neural networks, IEEE Transactions on Biomedical Engineering 63(3): 664–675.10.1109/TBME.2015.2468589
  20. Kiranyaz, S., Ince, T. and Gabbouj, M. (2015b). Real-time patient-specific ECG classification by 1-D convolutional neural networks, IEEE Transactions on Biomedical Engineering 63(3): 664–675.10.1109/TBME.2015.2468589
  21. Kohavi, R. and John, G.H. (1997). Wrappers for feature subset selection, Artificial Intelligence 97(1): 273–324.10.1016/S0004-3702(97)00043-X
  22. Koziarski, M. and Cyganek, B. (2018). Impact of low resolution on image recognition with deep neural networks: An experimental study, International Journal of Applied Mathematics and Computer Science 28(4): 735–744, DOI: 10.2478/amcs-2018-0056.10.2478/amcs-2018-0056
  23. Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2017). Imagenet classification with deep convolutional neural networks, Communications of the ACM 60(6): 84–90.10.1145/3065386
  24. Kusy, M., Zajdel, R., Kluska, J. and Zabinski, T. (2020). Fusion of feature selection methods for improving model accuracy in the milling process data classification problem, International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, pp. 1–8.
  25. LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning, Nature 521(7553): 436–444.10.1038/nature1453926017442
  26. LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998). Gradient-based learning applied to document recognition, Proceedings of the IEEE 86(11): 2278–2324.10.1109/5.726791
  27. Li, Y., Hsu, D.F. and Chung, S.M. (2013). Combination of multiple feature selection methods for text categorization by using combinatorial fusion analysis and rank-score characteristic, International Journal on Artificial Intelligence Tools 22(02): 1350001.10.1142/S0218213013500012
  28. Lu, J., Zhao, T. and Zhang, Y. (2008). Feature selection based-on genetic algorithm for image annotation, Knowledge-Based Systems 21(8): 887–891.10.1016/j.knosys.2008.03.051
  29. Mansouri, K., Ringsted, T., Ballabio, D., Todeschini, R. and Consonni, V. (2013). Quantitative structure–activity relationship models for ready biodegradability of chemicals, Journal of Chemical Information and Modeling 53(4): 867–878.10.1021/ci400021323469921
  30. Narendra, P.M. and Fukunaga, K. (1977). A branch and bound algorithm for feature subset selection, IEEE Transactions on Computers 26(09): 917–922.10.1109/TC.1977.1674939
  31. Pes, B. (2020). Ensemble feature selection for high-dimensional data: A stability analysis across multiple domains, Neural Computing and Applications 32(10): 5951–5973.10.1007/s00521-019-04082-3
  32. Robnik-Šikonja, M. and Kononenko, I. (2003). Theoretical and empirical analysis of ReliefF and RReliefF, Machine Learning 53(1–2): 23–69.10.1023/A:1025667309714
  33. Rodrigues, D., Pereira, L.A., Nakamura, R.Y., Costa, K.A., Yang, X.-S., Souza, A.N. and Papa, J.P. (2014). A wrapper approach for feature selection based on bat algorithm and optimum-path forest, Expert Systems with Applications 41(5): 2250–2258.10.1016/j.eswa.2013.09.023
  34. Rokach, L., Chizi, B. and Maimon, O. (2006). Feature selection by combining multiple methods, in M. Last et al. (Eds), Advances in Web Intelligence and Data Mining, Springer, Berlin/Heidelberg, pp. 295–304.10.1007/3-540-33880-2_30
  35. Russell, S. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach, Prentice Hall, Englewood Cliffs.
  36. Scherer, D., Müller, A. and Behnke, S. (2010). Evaluation of pooling operations in convolutional architectures for object recognition, International Conference on Artificial Neural Networks, Thessaloniki, Greece, pp. 92–101.
  37. Vergara, J.R. and Estévez, P.A. (2014). A review of feature selection methods based on mutual information, Neural Computing and Applications 24(1): 175–186.10.1007/s00521-013-1368-0
  38. Wang, Y., Zhang, D. and Dai, G. (2020). Classification of high resolution satellite images using improved U-Net, International Journal of Applied Mathematics and Computer Science 30(3): 399–413, DOI: 10.34768/amcs-2020-0030.
  39. Whitney, A.W. (1971). A direct method of nonparametric measurement selection, IEEE Transactions on Computers 100(9): 1100–1103.10.1109/T-C.1971.223410
  40. Wuniri, Q., Huangfu, W., Liu, Y., Lin, X., Liu, L. and Yu, Z. (2019). A generic-driven wrapper embedded with feature-type-aware hybrid Bayesian classifier for breast cancer classification, IEEE Access 7: 119931–119942.10.1109/ACCESS.2019.2932505
  41. Zajdel, R., Kusy, M., Kluska, J. and Zabinski, T. (2020). Weighted feature selection method for improving decisions in milling process diagnosis, in L. Rutkowski et al. (Eds), Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, Vol. 12415, Part I, Springer, Cham, pp. 280–291.10.1007/978-3-030-61401-0_27
DOI: https://doi.org/10.34768/amcs-2021-0047 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 685 - 696
Submitted on: Feb 24, 2021
Accepted on: Oct 4, 2021
Published on: Dec 30, 2021
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2021 Maciej Kusy, Roman Zajdel, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.