Model Applicability Across a Controlled Sample-Size–Class-Imbalance Space for Rockburst Classification

Rockburst intensity classification supports risk assessment in deep underground engineering, but its performance is often constrained by limited sample size and class imbalance. This study evaluates the response of five fixed classifier configurations to jointly controlled changes in sample size and class imbalance. A cleaned 331-case four-class database was used to construct 16 conditions defined by N=50, 100, 150, 192 and IRtarget=1, 2, 4, 6. RF, SVM, CW-SVM, SMOTE-SVM, and TabPFN were evaluated over 10 controlled repeats using shared four-fold stratified partitions. Macro-F1 was the primary metric, with balanced accuracy and MCC as complementary metrics. After study-wide Holm correction, the Friedman tests remained significant in 15 of 16 conditions for Macro-F1 and balanced accuracy and in 14 of 16 for MCC. TabPFN achieved the highest mean Macro-F1 in 10 conditions, RF in five, and SMOTE-SVM in one. None of TabPFN’s 10 mean leads over the second-ranked configuration were significant after condition-specific Holm correction. The only single-member Macro-F1 competitive set occurred at N=100 and IRtarget=6 for RF, while none of the 480 pairwise Wilcoxon tests remained significant after study-wide Holm correction. In the 12-case independent engineering case validation, CW-SVM achieved the highest accuracy and MCC, whereas TabPFN achieved the highest Macro-F1 and balanced accuracy. These results indicate condition-dependent mean performance and statistical competition without evidence of universal superiority among the evaluated configurations.