Abstract
Hierarchical structure, which is universal in nature and human society, can bring much benefit to classification tasks. Hierarchical classification typically first decomposes the multi-class classification problem into many smaller ones according to the hierarchical relationship lying in the classes, and then learns and organizes corresponding classifiers hierarchically. In such a framework, learning the class hierarchy is a critical and challenging step. In this paper, a k-ary class hierarchy construction approach is designed for multi-class and multi-feature scenario. An generalized adaptive kernel learning method is proposed to optimize the kernel fusion and k-way class partition together, and an iterative optimization algorithm is designed for it. Experimental results on synthetic and real datasets show the superiority of k-ary class hierarchy to binary class hierarchy.
Keywords
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Lu, Y., Lu, J., Wang, L., Yang, J. (2012). Constructing Class Hierarchy via Adaptive Kernel Learning. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_10
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DOI: https://doi.org/10.1007/978-3-642-33506-8_10
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