A Fine-Grained Random Forests using Class Decomposition
Elyan, Eyad and Gaber, Mohamed Medhat (2016) A Fine-Grained Random Forests using Class Decomposition. Neural Computing and Applications, 27 (8). p. 2279. ISSN 0941-0643
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Abstract
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 dataset without the need for feature engineering required by techniques like Support Ve]ctor Machines
(SVM) 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 datasets in that are mainly related to the medical domain. Results reported
in this paper shows clearly that our method has significantly improved the accuracy of Random Forests.
| Item Type: | Article | ||||||||
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| Additional Information: | The final publication is available at link.springer.com via https://doi.org/10.1007/s00521-015-2064-z | ||||||||
| Identification Number: | https://doi.org/10.1007/s00521-015-2064-z | ||||||||
| Dates: |
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| Uncontrolled Keywords: | Machine learning · Random Forests · Clustering · Ensemble learning | ||||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science | ||||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology |
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| Depositing User: | Ian Mcdonald | ||||||||
| Date Deposited: | 26 Jan 2017 11:44 | ||||||||
| Last Modified: | 22 Mar 2023 12:01 | ||||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/3829 |
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