Abstract
Mineral determination is a basis of the petrography. Automatic mineral classification based on digital image analysis is getting very popular. To improve classification accuracy we consider similarity features, complex one stage classifiers and two-stage classifiers based on simple pair-wise classification algorithms. Results show that employment of two-stage classifiers with proper parameters orK class single layer perceptron are good choices for mineral classification. Similarity features with properly selected parameters allow obtaining non-linear decision boundaries and lead to sizeable decrease in classification error rate.
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Kybartas, R., Baykan, N.A., Yilmaz, N., Raudys, S. (2010). Multiclass Mineral Recognition Using Similarity Features and Ensembles of Pair-Wise Classifiers. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13025-0_6
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DOI: https://doi.org/10.1007/978-3-642-13025-0_6
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