Support vector machine with parameter optimization by bare bones differential evolution

Dao-yin, Qiu; Li, Yao; Zhang, Xiaoyuan; Gu, Bing-Lin

doi:10.1109/icnc.2011.6022065

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…They attempt to solve two modified primal problems of TSVM, instead of two dual problems. Qiu et al, 51 have used bare bones differential evolution (BBDE) to tune the parameters of SVM. According to them the BBDE is a new optimization algorithm, which is a hybrid of the barebones particle swarm optimization (PSO) and differential evolution (DE).…”

Section: Related Workmentioning

confidence: 99%

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

Dash¹,

Sahoo²,

Dehuri

et al. 2015

Int. J. Artif. Intell. Tools

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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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Section: Related Workmentioning

confidence: 99%