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Research Article

Coal free-swelling index modeling by an ordinal-based soft computing approach

, ORCID Icon & ORCID Icon
Pages 769-793 | Received 10 Mar 2022, Accepted 03 May 2022, Published online: 16 May 2022

References

  • Astm-D3172. 2013. Standard practice for proximate analysis of coal and coke. ASTM International: 1-2.
  • Astm-D720-91. 1999. Test method for free-swelling index of coal. ASTM International: 226-30.
  • Bergstra, J., and Y. Bengio. 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research: JMLR 13:281–305.
  • Blengini, G. A., C. E. Latunussa, U. Eynard, C. Torres De Matos, D. Wittmer, K. Georgitzikis, C. Pavel, S. Carrara, L. Mancini, M. Unguru, et al. 2020. Study on the eu’s list of critical raw materials. Luxembourg: Publications Office of the European Union.
  • Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. 2002. Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16:321–57. doi:10.1613/jair.953.
  • Chelgani, S. C. 2018. Occurrences of valuable trace elements in iranian coals as potential coal combustion byproducts. International Journal of Coal Preparation and Utilization 41 (7):508–520 .
  • Chelgani, S. C., E. Hadavandi, and J. C. Hower. 2020. Study relationship between the coal thermoplastic factor with its organic and inorganic properties by the support vector regression method. International Journal of Coal Preparation and Utilization 40 (11):743–54. doi:10.1080/19392699.2017.1409215.
  • Chelgani, S. C., J. C. Hower, and B. Hart. 2011. Estimation of free-swelling index based on coal analysis using multivariable regression and artificial neural network. Fuel Processing Technology 92 (3):349–55. doi:10.1016/j.fuproc.2010.09.027.
  • Chelgani, S. C., S. Matin, and J. C. Hower. 2016a. Explaining relationships between coke quality index and coal properties by random forest method. Fuel 182:754–60. doi:10.1016/j.fuel.2016.06.034.
  • Chelgani, S. C., S. Matin, and S. Makaremi. 2016b. Modeling of free swelling index based on variable importance measurements of parent coal properties by random forest method. Measurement 94:416–22. doi:10.1016/j.measurement.2016.07.070.
  • Frank, E., and M. Hall. 2001. A simple approach to ordinal classification. In European Conference on Machine Learning, Freiburg, Germany.
  • Golzadeh, M., E. Hadavandi, and S. C. Chelgani. 2018. A new ensemble based multi-agent system for prediction problems: Case study of modeling coal free swelling index. Applied Soft Computing 64:109–25. doi:10.1016/j.asoc.2017.12.013.
  • Grandini, M., E. Bagli, and G. Visani. 2020. Metrics for multi-class classification: An overview. arXiv preprint arXiv:2008.05756, 1–17.
  • Guo, S., Y. Liu, R. Chen, X. Sun, and X. Wang. 2019. Improved smote algorithm to deal with imbalanced activity classes in smart homes. Neural Processing Letters 50 (2):1503–26. doi:10.1007/s11063-018-9940-3.
  • Guo, L., M. Zhai, Z. Wang, Y. Zhang, and P. Dong. 2018. Comprehensive coal quality index for evaluation of coal agglomeration characteristics. Fuel 231:379–86. doi:10.1016/j.fuel.2018.05.119.
  • Gutiérrez, P. A., M. Perez-Ortiz, J. Sanchez-Monedero, F. Fernandez-Navarro, and C. Hervas-Martinez. 2015. Ordinal regression methods: Survey and experimental study. IEEE Transactions on Knowledge and Data Engineering 28 (1):127–46. doi:10.1109/TKDE.2015.2457911.
  • Hadavandi, E., and S. C. Chelgani. 2019. Estimation of coking indexes based on parental coal properties by variable importance measurement and boosted-support vector regression method. Measurement 135:306–11. doi:10.1016/j.measurement.2018.11.068.
  • Han, H., W.-Y. Wang, and B.-H. Mao. 2005. Borderline-smote: A new over-sampling method in imbalanced data sets learning. In International conference on intelligent computing, Hefei, China.
  • He, H., Y. Bai, E. A. Garcia, and S. Li. 2008. Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), Hong Kong.
  • Hower, J. C., and C. F. Eble. 1996. Coal quality and coal utilization. Energy Minerals Division Hourglass 30 (7):1–8.
  • Jegierski, H., and S. Saganowski. 2020. An “outside the box” solution for imbalanced data classification. IEEE Access 8:125191–209. doi:10.1109/ACCESS.2020.3007801.
  • Khodayar, M., J. Wang, and M. Manthouri. 2018. Interval deep generative neural network for wind speed forecasting. IEEE Transactions on Smart Grid 10 (4):3974–89. doi:10.1109/TSG.2018.2847223.
  • Khorami, M. T., S. C. Chelgani, J. C. Hower, and E. Jorjani. 2011. Studies of relationships between free swelling index (fsi) and coal quality by regression and adaptive neuro fuzzy inference system. International Journal of Coal Geology 85 (1):65–71. doi:10.1016/j.coal.2010.09.011.
  • Khoshjavan, S., M. Heidary, and B. Rezai. 2010. Estimation of coal swelling index based on chemical properties of coal using artificial neural networks. Iranian Journal of Materials Science and Engineering 7:1–11.
  • Kotsiantis, S. B., and P. E. Pintelas. 2004. A cost sensitive technique for ordinal classification problems. In Hellenic Conference on Artificial Intelligence, Samos, Greece.
  • Lausser, L., L. M. Schäfer, S. D. Kühlwein, A. M. Kestler, and H. A. Kestler. 2020. Detecting ordinal subcascades. Neural Processing Letters 52 (3):2583–605. doi:10.1007/s11063-020-10362-0.
  • Matin, S. S., and S. C. Chelgani. 2016. Estimation of coal gross calorific value based on various analyses by random forest method. Fuel 177:274–78. doi:10.1016/j.fuel.2016.03.031.
  • Mohapatra, S., and S. Mohapatra. 2016. Machine learning approach for automated coal characterization using scanned electron microscopic images. Computers in Industry 75:35–45. doi:10.1016/j.compind.2015.10.003.
  • Napierala, K., and J. Stefanowski. 2016. Types of minority class examples and their influence on learning classifiers from imbalanced data. Journal of Intelligent Information Systems 46 (3):563–97. doi:10.1007/s10844-015-0368-1.
  • Palmer, C. A., C. L. Oman, A. J. Park, and J. A. Luppens. 2015. The U.S. Geological Survey coal quality (COALQUAL) database version 3.0: U.S. Geological Survey Data Series 975:43. with appendixes. doi:10.3133/ds975.
  • Pérez-Ortiz, M., M. Fernández-Delgado, E. Cernadas, R. Domínguez-Petit, P. A. Gutiérrez, and C. Hervás-Martínez. 2016. On the use of nominal and ordinal classifiers for the discrimination of states of development in fish oocytes. Neural Processing Letters 44 (2):555–70. doi:10.1007/s11063-015-9476-8.
  • Pirizadeh, M., N. Alemohammad, M. Manthouri, and M. Pirizadeh. 2021. A new machine learning ensemble model for class imbalance problem of screening enhanced oil recovery methods. Journal of Petroleum Science and Engineering 198:108214. doi:10.1016/j.petrol.2020.108214.
  • Pirizadeh, M., and M. Pirizadeh. 2021. Artificial intelligence applications in analyzing seismological data (case study: Precursors data). Disaster Prevention and Management Knowledge (Quarterly) 11:299–309.
  • Sáez, J. A., J. Luengo, J. Stefanowski, and F. Herrera. 2015. Smote–ipf: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Information Sciences 291:184–203. doi:10.1016/j.ins.2014.08.051.
  • Speight, J. G. 2005. Handbook of coal analysis. New Jersey, USA: John wiley & sons, inc.
  • Wang, S., and X. Yao. 2012. Multiclass imbalance problems: Analysis and potential solutions. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42 (4):1119–30. doi:10.1109/TSMCB.2012.2187280.
  • Yıldırım, P., U. K. Birant, and D. Birant. 2019. Eboc: Ensemble-based ordinal classification in transportation. Journal of Advanced Transportation 2019:1–17. doi:10.1155/2019/7482138.
  • Yu, H., C. Chen, and H. Yang. 2020. Two-stage game strategy for multiclass imbalanced data online prediction. Neural Processing Letters 52 (3):2493–512. doi:10.1007/s11063-020-10358-w.
  • Zhang, Z., B. Krawczyk, S. Garcia, A. Rosales-Perez, and F. Herrera. 2016. Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data. Knowledge-Based Systems 106:251–63. doi:10.1016/j.knosys.2016.05.048.

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