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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.<\/jats:p>","DOI":"10.1142\/s021821301550013x","type":"journal-article","created":{"date-parts":[[2015,2,6]],"date-time":"2015-02-06T01:42:49Z","timestamp":1423186969000},"page":"1550013","source":"Crossref","is-referenced-by-count":9,"title":["An Empirical Analysis of Evolved Radial Basis Function Networks and Support Vector Machines with Mixture of Kernels"],"prefix":"10.1142","volume":"24","author":[{"given":"Ch. 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