An Empirical Analysis of Evolved Radial Basis Function Networks and Support Vector Machines with Mixture of Kernels

Abstract

Classification is one of the most fundamental and formidable tasks in many domains including biomedical. In biomedical domain, the distributions of data in most of the datasets into predefined number of classes is significantly different (i.e., the classes are distributed unevenly). Many mathematical, statistical, and machine learning approaches have been developed for classification of biomedical datasets with a varying degree of success. This paper attempts to analyze the empirical performance of two forefront machine learning algorithms particularly designed for classification problem by adding some novelty to address the problem of imbalanced dataset. The evolved radial basis function network with novel kernel and support vector machine with mixture of kernels are suitably designed for the purpose of classification of imbalanced dataset. The experimental outcome shows that both algorithms are promising compared to simple radial basis function neural networks and support vector machine, respectively. However, on an average, support vector machine with mixture kernels is better than evolved radial basis function neural networks.

Extreme Learning Machine Based Prognostics of Battery Life

“…The second group of approaches aim to map from extracted features or stressing factors to state of health. These include the use of support vector machine 27,28 and neural networks. 29,30 These techniques can be easily implemented and have a good nonlinear mapping capability.…”

“…All these techniques are sensitive to the quantity and quality of the battery data. Several data-driven approaches, including support vector machines, 27,28 relevance vector machines, 33 and artificial neural networks 30 have been implemented for the prediction of remaining useful life of batteries. 34 However, many of such techniques suffer from computational complexity and higher computational time.…”

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

“…However, most of this research only focused on the SVM classifier with a single kernel function. Though some literature [20,21] indicates that combining multiple kernel functions can obtain better performance than a single kernel function, little research has provided an in-depth analysis of the performance of SVM classifier with a combined kernel function. There would therefore seem to be a definite need to systematically study the complex optimization problem in the SVM classifier with a combined kernel.In 2015, Mirjalili proposed a new meta-heuristic algorithm called the dragonfly algorithm (DA) [22], which has already been used to solve different optimization problems, such as feature selection [23,24], the knapsack problem [25], and image processing [26].…”

“…However, most of this research only focused on the SVM classifier with a single kernel function. Though some literature [20,21] indicates that combining multiple kernel functions can obtain better performance than a single kernel function, little research has provided an in-depth analysis of the performance of SVM classifier with a combined kernel function. There would therefore seem to be a definite need to systematically study the complex optimization problem in the SVM classifier with a combined kernel.…”