AdaBoost algorithm with random forests for predicting breast cancer survivability

Jaree THONGKAM, Guandong XU, Yanchun ZHANG

Research output: Chapter in Book/Report/Conference proceedingChapters

59 Citations (Scopus)

Abstract

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. Copyright © 2008 IEEE.

Original languageEnglish
Title of host publicationProceedings of 2008 International Joint Conference on Neural Networks, IJCNN 2008
Place of PublicationUSA
PublisherIEEE
Pages3062-3069
ISBN (Print)9781424418213
DOIs
Publication statusPublished - 2008

Citation

Thongkam, J., Xu, G., & Zhang, Y. (2008). AdaBoost algorithm with random forests for predicting breast cancer survivability. In Proceedings of 2008 International Joint Conference on Neural Networks, IJCNN 2008 (pp. 3062-3069). IEEE. https://doi.org/10.1109/IJCNN.2008.4634231

Fingerprint

Dive into the research topics of 'AdaBoost algorithm with random forests for predicting breast cancer survivability'. Together they form a unique fingerprint.