Comparative Performance Analysis of Ensemble Learning Algorithms for Rock Classification

Authors : Ebru Efeoğlu

Pages : 61-66

Doi:10.55385/kastamonujes.1355695

View : 147 | Download : 66

Publication Date : 2023-12-26

Article Type : Research Paper

Abstract :Knowing the physical and mechanical properties of rocks is important for engineering studies. Because determining the properties and type of rocks affects the safety of engineering structures. Therefore, this study is important in terms of minimizing possible errors in engineering studies. Moreover, Automatic detection of rock types reduces the workload of engineers. In this study, the types of rocks were determined by using some physical and mechanical properties of rocks measured in the laboratory. Rep tree algorithm and ensemble learning algorithms were used in the study. The success of ensemble learning algorithms in classification was compared. As a result, it was understood that ensemble learning algorithms increase success. When the logitboost algorithm was used together with the rep tree algorithm, the Tp rate increased to 0.82. Precision Recall values were 0.80, MCC and AUC were 0.95, kappa was 0.80. In addition, the FP rate decreased to 0.04. The most successful algorithm in rock classification was the Logistboost algorithm. The highest performance metrics were obtained in the classification made with the Logistboost algorithm. In addition, 4 different metric types were calculated to determine the error rates of the algorithms. Logistboost algorithm classified with the lowest error rate.
Keywords : Clasification, Ensemble learning algorithms, Machine learning, Rep tree, Rock

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