TY - JOUR
T1 - An Empirical Analysis of Evolved Radial Basis Function Networks and Support Vector Machines with Mixture of Kernels
AU - Dash, Ch Sanjeev Kumar
AU - Sahoo, Pulak
AU - Dehuri, Satchidananda
AU - Cho, Sung Bae
N1 - Publisher Copyright:
© 2015 World Scientific Publishing Company.
PY - 2015/8/24
Y1 - 2015/8/24
N2 - 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.
AB - 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.
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U2 - 10.1142/s021821301550013x
DO - 10.1142/s021821301550013x
M3 - Article
AN - SCOPUS:84940033890
VL - 24
JO - International Journal on Artificial Intelligence Tools
JF - International Journal on Artificial Intelligence Tools
SN - 0218-2130
IS - 4
M1 - 1550013
ER -