312
ment between AUC and Patient sensitivity as to the
performance of the classification algorithms. The
SVM depends on the kernel parametrization but
together with NB are able to deliver 100% sensitiv-
ity. We find that NB achieved the best performance
with higher AUC and lower FPR. The feature rank-
ing seems a good solution to reduce the dimension
of the dataset but retaining the necessary informa-
tion to get high classification performance.
REFERENCES
Barandela, R., R.M. Valdovinos, J.S. Sanchez, & F.J.
Ferri (2004). The imbalanced training sample prob-
lem: Under or over sampling? In Structural, Syntac-
tic, and Statistical Pattern Recognition, pp. 806–814.
Springer. Bellaachia, A. & E. Guven (2006). Predict-
ing breast cancer survivability using ...