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A fine-grained Random Forests using class decomposition: an application to medical diagnosis.

Elyan, Eyad; Gaber, Mohamed Medhat


Mohamed Medhat Gaber


Class decomposition describes the process of segmenting each class into a number of homogeneous subclasses. This can be naturally achieved through clustering. Utilising class decomposition can provide a number of benefits to supervised learning, especially ensembles. It can be a computationally efficient way to provide a linearly separable data set without the need for feature engineering required by techniques like support vector machines and deep learning. For ensembles, the decomposition is a natural way to increase diversity, a key factor for the success of ensemble classifiers. In this paper, we propose to adopt class decomposition to the state-of-the-art ensemble learning Random Forests. Medical data for patient diagnosis may greatly benefit from this technique, as the same disease can have a diverse of symptoms. We have experimentally validated our proposed method on a number of data sets that are mainly related to the medical domain. Results reported in this paper show clearly that our method has significantly improved the accuracy of Random Forests.


ELYAN, E. and GABER, M.M. 2015. A fine-grained Random Forests using class decomposition: an application to medical diagnosis. Neural computing and applications [online], 27(8), pages 2279-2288. Available from:

Journal Article Type Article
Acceptance Date Sep 8, 2015
Online Publication Date Sep 22, 2015
Publication Date Nov 1, 2016
Deposit Date Sep 9, 2016
Publicly Available Date Sep 23, 2016
Journal Neural computing and applications
Print ISSN 0941-0643
Electronic ISSN 1433-3058
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 27
Issue 8
Pages 2279-2288
Keywords Machine learning; Random Forests; Clustering; Ensemble learning
Public URL


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