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.
|Journal Article Type||Article|
|Publication Date||Nov 1, 2016|
|Journal||Neural computing and applications|
|Publisher||Springer (part of Springer Nature)|
|Peer Reviewed||Peer Reviewed|
|Institution Citation||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: https://doi.org/10.1007/s00521-015-2064-z|
|Keywords||Machine learning; Random Forests; Clustering; Ensemble learning|
ELYAN 2015 A fine-grained random forests