Isolation-based hyperbox granular classification computing
Bottom-up and top-down are two main computing models in granular computing by which the granule set including granules with different granularities. The top-down hyperbox granular computing classification algorithm based on isolation, or IHBGrC for short, is proposed in the framework of top-down com...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2017-06-01
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| Series: | Journal of Algorithms & Computational Technology |
| Online Access: | https://doi.org/10.1177/1748301816676818 |
| Summary: | Bottom-up and top-down are two main computing models in granular computing by which the granule set including granules with different granularities. The top-down hyperbox granular computing classification algorithm based on isolation, or IHBGrC for short, is proposed in the framework of top-down computing model. Algorithm IHBGrC defines a novel function to measure the distance between two hyperbox hgranules, which is used to judge the inclusion relation between two hyperbox granules, the meet operation is used to isolate the i th class data from the other class data, and the hyperbox granule is partitioned into some hyperbox granules which include the i th class data. We compare the performance of IHBGrC with support vector machines and HBGrC, for a number of two-class problems and multiclass problems. Our computational experiments showed that IHBGrC can both speed up training and achieve comparable generalization performance. |
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| ISSN: | 1748-3018 1748-3026 |