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AdaBoost algorithm with random forests for predicting breast cancer survivability

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3 Author(s)
Thongkam, J. ; Sch. of Comput. Sci. & Math., Victoria Univ., Melbourne, VIC ; Guandong Xu ; Yanchun Zhang

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.

Published in:

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Date of Conference:

1-8 June 2008