Deducing Optimal Classification Algorithm for Heterogeneous Fabric

For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose. To help future researchers, we describe in this paper the optimal among the best of the algorithms. We built a synthetic data set and performed the supervised machine learning runs for five different algorithms. For heterogeneous rock fabric, we identified Random Forest, among others, to be the appropriate algorithm.

[1]  Igor Varfolomeev,et al.  An Application of Deep Neural Networks for Segmentation of Microtomographic Images of Rock Samples , 2019, Comput..

[2]  Martin J. Blunt,et al.  Reconstruction of three-dimensional porous media using generative adversarial neural networks , 2017, Physical review. E.

[3]  Tie-Yan Liu,et al.  LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.

[4]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[5]  Massimiliano Bonomi,et al.  Metainference: A Bayesian inference method for heterogeneous systems , 2015, Science Advances.

[6]  Shruti Mishra,et al.  Machine learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets , 2018, Science Advances.

[7]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[8]  John W. Fisher,et al.  Learning from Examples with Information Theoretic Criteria , 2000, J. VLSI Signal Process..

[9]  David Ray Anderson.  Modern Business Statistics with Microsoft Excel , 2020 .

[10]  Mohamed Sassi,et al.  Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination , 2021, ArXiv.

[11]  Andreas Müller,et al.  Introduction to Machine Learning with Python: A Guide for Data Scientists , 2016 .

[12]  Xing Xu,et al.  Cretaceous age for the feathered dinosaurs of Liaoning, China , 1999, Nature.

[13]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.