Abstract
This chapter is focused on the insufficient performance of the hierarchical classifiers. The theory of granular computing and the mathematical model of the piecewise-regular object from Chap. 2 are used to define the hierarchical levels. The sequence with a small number of the weakly homogeneous segments is considered as a coarse-grained granule. Fine-grained granules stand for a large number of high-homogeneous small segments. We apply sequential three-way decisions (TWD) to speed-up the classification procedure. To improve the classification performance at each granularity level, the probabilistic rough set of the distance between objects from different classes at each level is created. If the distance between an observed object and the next checked instance is included in its negative region, the search procedure is terminated. Experimental results demonstrated that sequential TWD significantly decreases the classification time in comparison with the matching of the pyramid histograms of oriented gradients.
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Savchenko, A.V. (2016). Hierarchical Intelligent Classification Systems. In: Search Techniques in Intelligent Classification Systems. SpringerBriefs in Optimization. Springer, Cham. https://doi.org/10.1007/978-3-319-30515-8_3
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DOI: https://doi.org/10.1007/978-3-319-30515-8_3
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