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 |
| _version_ | 1828474255645343744 |
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| author | Hongbing Liu Fan Zhang Ran Li Chang-an Wu |
| author_facet | Hongbing Liu Fan Zhang Ran Li Chang-an Wu |
| author_sort | Hongbing Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T05:56:58Z |
| format | Article |
| id | doaj.art-179c4e7bbb47443abde6413006b63dac |
| institution | Directory Open Access Journal |
| issn | 1748-3018 1748-3026 |
| language | English |
| last_indexed | 2024-12-11T05:56:58Z |
| publishDate | 2017-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Algorithms & Computational Technology |
| spelling | doaj.art-179c4e7bbb47443abde6413006b63dac2022-12-22T01:18:37ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30181748-30262017-06-011110.1177/174830181667681810.1177_1748301816676818Isolation-based hyperbox granular classification computingHongbing LiuFan ZhangRan LiChang-an WuBottom-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.https://doi.org/10.1177/1748301816676818 |
| spellingShingle | Hongbing Liu Fan Zhang Ran Li Chang-an Wu Isolation-based hyperbox granular classification computing Journal of Algorithms & Computational Technology |
| title | Isolation-based hyperbox granular classification computing |
| title_full | Isolation-based hyperbox granular classification computing |
| title_fullStr | Isolation-based hyperbox granular classification computing |
| title_full_unstemmed | Isolation-based hyperbox granular classification computing |
| title_short | Isolation-based hyperbox granular classification computing |
| title_sort | isolation based hyperbox granular classification computing |
| url | https://doi.org/10.1177/1748301816676818 |
| work_keys_str_mv | AT hongbingliu isolationbasedhyperboxgranularclassificationcomputing AT fanzhang isolationbasedhyperboxgranularclassificationcomputing AT ranli isolationbasedhyperboxgranularclassificationcomputing AT changanwu isolationbasedhyperboxgranularclassificationcomputing |