Abstract
If all bounding hyper-spheres for training data of every class are independent, classification for any test sample is easy to compute with high classification accuracy. But real application data are very complicated and relationships between classification bounding spheres are very complicated too. Based on detailed analysis of relationships between bounding hyper-spheres, a hybrid decision strategy is put forward to solve classification problem of the intersections for multi-class classification based on hyper-sphere support vector machines. First, characteristics of data distribution in the intersections are analyzed and then decision class is decided by different strategies. If training samples of two classes in the intersection can be classified by intersection hyper-plane for two hyper-spheres, then new test samples can be decided by this plane. If training samples of two classes in the intersection can be approximately linearly classified, new test samples can be classified by standard optimal binary-SVM hyper-plane. If training samples of two classes in the intersection cannot be linearly classified, new test samples can be decided by introducing kernel function to get optimal classification hyper-plane. If training examples belong to only one class, then new test samples can be classified by exclusion method. Experimental results show performance of our algorithm is more optimal than hyper-sphere support vector machines with only one decision strategy with relatively low computation cost.
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Acknowledgement
This research was funded by the Natural Science Foundation of Liaoning Province, China (grant no. 2019-ZD-0175).
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Liu, S., Chen, P. (2020). Hyper-Sphere Support Vector Classifier with Hybrid Decision Strategy. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_8
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DOI: https://doi.org/10.1007/978-3-030-63820-7_8
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