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Both neural networks and decision trees are popular machine learning methodsand are widely used to solve problems from diverse domains. These twoclassifiers are commonly used base classifiers in an ensemble framework. Inthis paper, we first present a new variant of oblique decision tree based on alinear classifier, then construct an ensemble classifier based on the fusion ofa fast neural network, random vector functional link network and obliquedecision trees. Random Vector Functional Link Network has an elegant closedform solution with extremely short training time. The neural network partitionseach training bag (obtained using bagging) at the root level into C subsetswhere C is the number of classes in the dataset and subsequently, C obliquedecision trees are trained on such partitions. The proposed method provides arich insight into the data by grouping the confusing or hard to classifysamples for each class and thus, provides an opportunity to employ fine-grainedclassification rule over the data. The performance of the ensemble classifieris evaluated on several multi-class datasets where it demonstrates a superiorperformance compared to other state-of- the-art classifiers.

Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision Surfaces