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An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

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Abstract

In this paper, an interval type-2 Takagi-Sugeno fuzzy classification system (IT2T-SFCS) learned by particle swarm optimization (PSO) and support vector machine (SVM) for antecedent and consequent parameters optimization is proposed. The IT2T-SFCS is constructed by fuzzy if-then rules whose antecedents are interval type-2 fuzzy sets and consequents are linear state equations. The antecedents of IT2T-SFCS use the fuzzy iterative self-organizing data analysis technique (ISODATA) and PSO to learn and calculate the optimal centers and the uncertain widths of the Gaussian membership functions. Consequent parameters in IT2T-SFCS are learned through SVM for the purpose of achieving higher generalization ability. The proposed IT2T-SFCS is able to directly handle uncertainties, minimize the effects of uncertainties and get the better generalization performance, which inherits the benefits of interval type-2 T-S fuzzy system and SVM. For demonstration, IT2T-SFCS is used as a classifier in gender recognition. The experimental results show that the performance of the proposed IT2T-SFCS is superior to that of the previous mainstream classifiers.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61374194), the National Natural Science Foundation of China (No. 61403081), China Postdoctoral Science Foundation Founded Project (No. 2013 M540405), the Natural Science Foundation of Jiangsu Province (No. BK20140638), and Special Program of China Postdoctoral Science Foundation (No.2014T70454).

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Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, China

    Yijun Du, Xiaobo Lu, Lin Chen & Weili Zeng

  2. Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing, 210096, China

    Yijun Du, Xiaobo Lu, Lin Chen & Weili Zeng

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  1. Yijun Du
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  2. Xiaobo Lu
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  3. Lin Chen
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  4. Weili Zeng
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Correspondence to Xiaobo Lu.

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Du, Y., Lu, X., Chen, L. et al. An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition. Multimed Tools Appl 75, 987–1007 (2016). https://doi.org/10.1007/s11042-014-2338-y

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