A fine-grained Random Forests using class decomposition: an application to medical diagnosis. Issue 8 (November 2016)

• Record Type: Journal Article
• Authors: Elyan, Eyad; Gaber, Mohamed
• Is Part Of: Neural computing & applications. Volume 27:Issue 8(2016)
• Page Start: 2279
• Page End: 2288
• Publication Date: 2016-11
• Subjects: Machine learning -- Random Forests -- Clustering -- Ensemble learning; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2064-z
• Languages: English
• ISSNs: 0941-0643

Abstract

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 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.