AdaBoost algorithm with random forests for predicting breast cancer survivability

Thongkam, J; Xu, G; Zhang, Y

AdaBoost algorithm with random forests for predicting breast cancer survivability

Thongkam, J Xu, G

Zhang, Y

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2008, pp. 3062 - 3069
Issue Date:: 2008-11-24

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Field	Value	Language
dc.contributor.author	Thongkam, J	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Zhang, Y	en_US
dc.date.issued	2008-11-24	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2008, pp. 3062 - 3069	en_US
dc.identifier.isbn	9781424418213	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29982
dc.identifier.uri	http://hdl.handle.net/10453/33794
dc.description.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. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2008.4634231	en_US
dc.title	AdaBoost algorithm with random forests for predicting breast cancer survivability	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Hong Kong, China	en_US
dc.location.activity	Hong Kong, PEOPLES R CHINA
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

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. © 2008 IEEE.

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http://hdl.handle.net/10453/29982