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

Ch Sanjeev Kumar Dash, Pulak Sahoo, Satchidananda Dehuri, Sung Bae Cho

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Article number1550013
JournalInternational Journal on Artificial Intelligence Tools
Volume24
Issue number4
DOIs
Publication statusPublished - 2015 Aug 24

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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