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Protein Secondary Structure Prediction Based on Improved C-SVM for Unbalanced Datasets

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Proceedings of International Conference on Soft Computing Techniques and Engineering Application

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 250))

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Abstract

When protein secondary structure is predicted, the number of helix, sheet, and coiled coil is quite different in datasets. A multiple classifier based on improved clustering support vector machine (C-SVM) algorithm is proposed to predict protein secondary structure for unbalanced training datasets. Firstly, different weights are used for different types of samples to improve classification accuracy of traditional C-SVM on unbalanced samples. Secondly, the multiple classification strategy of one-versus-one (OVO) is used to build three binary classifiers. These binary classifiers are H/E, H/C, and E/C. The Majority-Voting law is used to integrate the results of three binary classifiers. Finally, sevenfold cross-validation based on grid method is used to optimize the parameters of classifiers. Simulation results show that, compared with the other prediction methods, the classification method proposed in this paper can obtain better classification accuracy on unbalanced datasets.

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Authors and Affiliations

  1. College of Computer and Information Engineering, Henan Normal University, No. 46, Eastern Construction Road, Xinxiang, Henan, China

    Ao Pei

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  1. Ao Pei
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Correspondence to Ao Pei .

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Editors and Affiliations

  1. Dept of Computer Science and Engineering, SOA University, Bhubaneswar, Orissa, India

    Srikanta Patnaik

  2. Electronics and Computer Engg Tech., Indiana State University, Indiana, Indiana, USA

    Xiaolong Li

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Pei, A. (2014). Protein Secondary Structure Prediction Based on Improved C-SVM for Unbalanced Datasets. In: Patnaik, S., Li, X. (eds) Proceedings of International Conference on Soft Computing Techniques and Engineering Application. Advances in Intelligent Systems and Computing, vol 250. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1695-7_58

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  • DOI: https://doi.org/10.1007/978-81-322-1695-7_58

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1694-0

  • Online ISBN: 978-81-322-1695-7

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