In this paper we propose a combination of the
AdaBoost and random forests algorithms for constructing a
breast cancer survivability prediction model. We use random
forests as a weak learner of AdaBoost for selecting the high
weight instances during the boosting process to improve
accuracy, stability and to reduce overfitting problems. The
capability of this hybrid method is evaluated using basic
performance measurements (e.g., accuracy, sensitivity, and
specificity), Receiver Operating Characteristic (ROC) curve
and Area Under the receiver operating characteristic Curve
(AUC). Experimental results indicate that the proposed method
outperforms a single classifier and other combined classifiers
for the breast cancer survivability prediction. --Conference held in Hong Kong