Incremental Maximum Gaussian Mixture Partition For Classification Article Swipe
Xianbin Hong
,
Jiehao Zhang
,
Sheng-Uei Guan
,
Di Yao
,
Nian Xue
,
Xuan Zhao
,
Xin Huang
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.2991/jimec-17.2017.31
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.2991/jimec-17.2017.31
In the field of classification, the main task of most algorithms is to find a perfect decision boundary.However, most decision boundaries are too complex to be discovered directly.Therefore, in this paper, we proposed an Incremental Maximum Gaussian Mixture Partition (IMGMP) algorithm for classification, aiming to solve those problems with complex decision boundaries.As a self-adaptive algorithm, it uses a divide and conquer strategy to calculate out a reasonable decision boundary by step.An Improved K-means clustering and a Maximum Gaussian Mixture model are used in the classifier.This algorithm also has been tested on artificial and real-life datasets in order to evaluate its remarkable flexibility and robustness.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2991/jimec-17.2017.31
- https://download.atlantis-press.com/article/25880535.pdf
- OA Status
- gold
- Cited By
- 7
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2743254441
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2743254441Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2991/jimec-17.2017.31Digital Object Identifier
- Title
-
Incremental Maximum Gaussian Mixture Partition For ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-01-01Full publication date if available
- Authors
-
Xianbin Hong, Jiehao Zhang, Sheng-Uei Guan, Di Yao, Nian Xue, Xuan Zhao, Xin HuangList of authors in order
- Landing page
-
https://doi.org/10.2991/jimec-17.2017.31Publisher landing page
- PDF URL
-
https://download.atlantis-press.com/article/25880535.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://download.atlantis-press.com/article/25880535.pdfDirect OA link when available
- Concepts
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Partition (number theory), Mixture model, Pattern recognition (psychology), Computer science, Gaussian, Artificial intelligence, Mathematics, Combinatorics, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 4, 2020: 1, 2019: 1, 2016: 1Per-year citation counts (last 5 years)
- References (count)
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29Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.paper, | 30 |
| abstract_inverted_index.tested | 89 |
| abstract_inverted_index.(IMGMP) | 39 |
| abstract_inverted_index.K-means | 72 |
| abstract_inverted_index.Maximum | 35, 76 |
| abstract_inverted_index.Mixture | 37, 78 |
| abstract_inverted_index.complex | 23, 49 |
| abstract_inverted_index.conquer | 60 |
| abstract_inverted_index.perfect | 15 |
| abstract_inverted_index.step.An | 70 |
| abstract_inverted_index.Gaussian | 36, 77 |
| abstract_inverted_index.Improved | 71 |
| abstract_inverted_index.boundary | 68 |
| abstract_inverted_index.datasets | 94 |
| abstract_inverted_index.decision | 16, 19, 50, 67 |
| abstract_inverted_index.evaluate | 98 |
| abstract_inverted_index.problems | 47 |
| abstract_inverted_index.proposed | 32 |
| abstract_inverted_index.strategy | 61 |
| abstract_inverted_index.Partition | 38 |
| abstract_inverted_index.algorithm | 40, 85 |
| abstract_inverted_index.calculate | 63 |
| abstract_inverted_index.real-life | 93 |
| abstract_inverted_index.algorithm, | 54 |
| abstract_inverted_index.algorithms | 10 |
| abstract_inverted_index.artificial | 91 |
| abstract_inverted_index.boundaries | 20 |
| abstract_inverted_index.clustering | 73 |
| abstract_inverted_index.discovered | 26 |
| abstract_inverted_index.reasonable | 66 |
| abstract_inverted_index.remarkable | 100 |
| abstract_inverted_index.Incremental | 34 |
| abstract_inverted_index.flexibility | 101 |
| abstract_inverted_index.robustness. | 103 |
| abstract_inverted_index.boundaries.As | 51 |
| abstract_inverted_index.self-adaptive | 53 |
| abstract_inverted_index.classification, | 4, 42 |
| abstract_inverted_index.classifier.This | 84 |
| abstract_inverted_index.boundary.However, | 17 |
| abstract_inverted_index.directly.Therefore, | 27 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5021761370 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I69356397 |
| citation_normalized_percentile.value | 0.706275 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |