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Table 3 Results for multi-class fold prediction in the fold recognition set-up. Results for the adaptive codes method are reported on a SCOP benchmark data set (26 folds, 303 superfamilies, 614 test sequences).

From: SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

Method (and optimization target) Error Balanced Error Top 5 Error Balanced Top 5 Error Recognition Rate at fdr = 1% Recognition Rate at fdr = 5%
PSI-BLAST 0.6482 0.7029 0.5179 0.5431 0.0814 0.0961
one-vs-all: Folds 0.4625 0.6282 0.1450 0.2345 0.1368 0.2704
one-vs-all: Folds, Sfams 0.4625 0.6282 0.1450 0.2345 0.1368 0.2704
Sigmoid Fitting: Folds 0.4446 0.6103 0.1547 0.2960 0.1336 0.2166
Adaptive Codes: Folds (zero-one) 0.4023 0.5556 0.1059 0.1543 0.1906 0.2655
Adaptive Codes: Folds (balanced) 0.3664 0.5158 0.1075 0.1387 0.1612 0.2785
Adaptive Codes: Folds, Sfams (zero-one) 0.4104 0.5525 0.1107 0.1719 0.2003 0.2329
Adaptive Codes: Folds, Sfams (balanced) 0.3616 0.5153 0.1010 0.1263 0.2068 0.2508
Adaptive Codes: Folds, Sfams, Fams (zero-one) 0.4007 0.5427 0.1075 0.1788 0.2068 0.2557
Adaptive Codes: Folds, Sfams, Fams (balanced) 0.3648 0.5000 0.1091 0.1453 0.2134 0.3013