A fine-grained random forests using class decomposition: an application to medical diagnosis.
Gaber, Mohamed Medhat
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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], FirstOnline. Available from: http://dx.doi.org/10.1007/s00521-015-2064-z
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.