A geometric-based data reduction approach for large low dimensional datasets: Delaunay triangulation in SVM algorithms Article Swipe
Omid Naghash Almasi
,
Modjtaba Rouhani
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.mlwa.2021.100025
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.mlwa.2021.100025
Training a support vector machine (SVM) on large datasets is a slow daunting process. Further, SVM becomes slow in the testing phase, due to its large number of support vectors (SVs). This paper proposes an effective geometric algorithm based on construction of Delaunay triangulation (DT) algorithm using Quickhull algorithm with a novel strategy to exactly identify and extract the boundary data points laid between the two classes of a dataset, and later uses these most informative data points as a reduced dataset to solve various SVM algorithms and proposes new DT-SVM algorithms Two synthetic datasets with the size of 1K incrementally up to 500K datasets are generated to extensively verify the effectiveness of the proposed DT-SVM algorithms over various data sizes and for further assessment, the most efficient version of proposed DT-SVM is applied on well-known benchmark datasets from UCI Machine Learning Repository. Two variant of sequential minimization optimization (SMO) decomposition methods, in addition to Least Square form of SVM are implemented to present the scalability of new DT-SVM algorithms in linear/nonlinear separable/non-separable large low dimensional datasets. Moreover, the most efficient version of the proposed algorithm is compared to RCH-SK as a known geometric approach in the SVM literature. The results demonstrate that while the proposed approach improves the scalability of DT-SVM in large low dimensional datasets, it leads SVM algorithms to maintain the accuracy in an acceptable range with considerably lower time in both training and testing phases with using a noticeably fewer number of SVs.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.mlwa.2021.100025
- OA Status
- gold
- Cited By
- 12
- References
- 61
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3134208004Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.mlwa.2021.100025Digital Object Identifier
- Title
-
A geometric-based data reduction approach for large low dimensional datasets: Delaunay triangulation in SVM algorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-07Full publication date if available
- Authors
-
Omid Naghash Almasi, Modjtaba RouhaniList of authors in order
- Landing page
-
https://doi.org/10.1016/j.mlwa.2021.100025Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.mlwa.2021.100025Direct OA link when available
- Concepts
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Support vector machine, Algorithm, Computer science, Scalability, Benchmark (surveying), Reduction (mathematics), Data point, Artificial intelligence, Delaunay triangulation, Pattern recognition (psychology), Machine learning, Data mining, Mathematics, Database, Geography, Geodesy, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 4, 2022: 6Per-year citation counts (last 5 years)
- References (count)
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61Number 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.is | 9, 132, 186 |
| abstract_inverted_index.it | 217 |
| abstract_inverted_index.of | 27, 41, 67, 98, 112, 129, 145, 158, 166, 182, 210, 245 |
| abstract_inverted_index.on | 6, 39, 134 |
| abstract_inverted_index.to | 23, 53, 82, 102, 107, 154, 162, 188, 221 |
| abstract_inverted_index.up | 101 |
| abstract_inverted_index.SVM | 15, 85, 159, 197, 219 |
| abstract_inverted_index.The | 199 |
| abstract_inverted_index.Two | 92, 143 |
| abstract_inverted_index.UCI | 139 |
| abstract_inverted_index.and | 56, 70, 87, 121, 236 |
| abstract_inverted_index.are | 105, 160 |
| abstract_inverted_index.due | 22 |
| abstract_inverted_index.for | 122 |
| abstract_inverted_index.its | 24 |
| abstract_inverted_index.low | 174, 214 |
| abstract_inverted_index.new | 89, 167 |
| abstract_inverted_index.the | 19, 58, 64, 96, 110, 113, 125, 164, 178, 183, 196, 204, 208, 223 |
| abstract_inverted_index.two | 65 |
| abstract_inverted_index.(DT) | 44 |
| abstract_inverted_index.500K | 103 |
| abstract_inverted_index.SVs. | 246 |
| abstract_inverted_index.This | 31 |
| abstract_inverted_index.both | 234 |
| abstract_inverted_index.data | 60, 76, 119 |
| abstract_inverted_index.form | 157 |
| abstract_inverted_index.from | 138 |
| abstract_inverted_index.laid | 62 |
| abstract_inverted_index.most | 74, 126, 179 |
| abstract_inverted_index.over | 117 |
| abstract_inverted_index.size | 97 |
| abstract_inverted_index.slow | 11, 17 |
| abstract_inverted_index.that | 202 |
| abstract_inverted_index.time | 232 |
| abstract_inverted_index.uses | 72 |
| abstract_inverted_index.with | 49, 95, 229, 239 |
| abstract_inverted_index.(SMO) | 149 |
| abstract_inverted_index.(SVM) | 5 |
| abstract_inverted_index.Least | 155 |
| abstract_inverted_index.based | 38 |
| abstract_inverted_index.fewer | 243 |
| abstract_inverted_index.known | 192 |
| abstract_inverted_index.large | 7, 25, 173, 213 |
| abstract_inverted_index.later | 71 |
| abstract_inverted_index.leads | 218 |
| abstract_inverted_index.lower | 231 |
| abstract_inverted_index.novel | 51 |
| abstract_inverted_index.paper | 32 |
| abstract_inverted_index.range | 228 |
| abstract_inverted_index.sizes | 120 |
| abstract_inverted_index.solve | 83 |
| abstract_inverted_index.these | 73 |
| abstract_inverted_index.using | 46, 240 |
| abstract_inverted_index.while | 203 |
| abstract_inverted_index.(SVs). | 30 |
| abstract_inverted_index.DT-SVM | 90, 115, 131, 168, 211 |
| abstract_inverted_index.RCH-SK | 189 |
| abstract_inverted_index.Square | 156 |
| abstract_inverted_index.number | 26, 244 |
| abstract_inverted_index.phase, | 21 |
| abstract_inverted_index.phases | 238 |
| abstract_inverted_index.points | 61, 77 |
| abstract_inverted_index.vector | 3 |
| abstract_inverted_index.verify | 109 |
| abstract_inverted_index.Machine | 140 |
| abstract_inverted_index.applied | 133 |
| abstract_inverted_index.becomes | 16 |
| abstract_inverted_index.between | 63 |
| abstract_inverted_index.classes | 66 |
| abstract_inverted_index.dataset | 81 |
| abstract_inverted_index.exactly | 54 |
| abstract_inverted_index.extract | 57 |
| abstract_inverted_index.further | 123 |
| abstract_inverted_index.machine | 4 |
| abstract_inverted_index.present | 163 |
| abstract_inverted_index.reduced | 80 |
| abstract_inverted_index.results | 200 |
| abstract_inverted_index.support | 2, 28 |
| abstract_inverted_index.testing | 20, 237 |
| abstract_inverted_index.variant | 144 |
| abstract_inverted_index.various | 84, 118 |
| abstract_inverted_index.vectors | 29 |
| abstract_inverted_index.version | 128, 181 |
| abstract_inverted_index.Delaunay | 42 |
| abstract_inverted_index.Further, | 14 |
| abstract_inverted_index.Learning | 141 |
| abstract_inverted_index.Training | 0 |
| abstract_inverted_index.accuracy | 224 |
| abstract_inverted_index.addition | 153 |
| abstract_inverted_index.approach | 194, 206 |
| abstract_inverted_index.boundary | 59 |
| abstract_inverted_index.compared | 187 |
| abstract_inverted_index.dataset, | 69 |
| abstract_inverted_index.datasets | 8, 94, 104, 137 |
| abstract_inverted_index.daunting | 12 |
| abstract_inverted_index.identify | 55 |
| abstract_inverted_index.improves | 207 |
| abstract_inverted_index.maintain | 222 |
| abstract_inverted_index.methods, | 151 |
| abstract_inverted_index.process. | 13 |
| abstract_inverted_index.proposed | 114, 130, 184, 205 |
| abstract_inverted_index.proposes | 33, 88 |
| abstract_inverted_index.strategy | 52 |
| abstract_inverted_index.training | 235 |
| abstract_inverted_index.Moreover, | 177 |
| abstract_inverted_index.Quickhull | 47 |
| abstract_inverted_index.algorithm | 37, 45, 48, 185 |
| abstract_inverted_index.benchmark | 136 |
| abstract_inverted_index.datasets, | 216 |
| abstract_inverted_index.datasets. | 176 |
| abstract_inverted_index.effective | 35 |
| abstract_inverted_index.efficient | 127, 180 |
| abstract_inverted_index.generated | 106 |
| abstract_inverted_index.geometric | 36, 193 |
| abstract_inverted_index.synthetic | 93 |
| abstract_inverted_index.acceptable | 227 |
| abstract_inverted_index.algorithms | 86, 91, 116, 169, 220 |
| abstract_inverted_index.noticeably | 242 |
| abstract_inverted_index.sequential | 146 |
| abstract_inverted_index.well-known | 135 |
| abstract_inverted_index.Repository. | 142 |
| abstract_inverted_index.assessment, | 124 |
| abstract_inverted_index.demonstrate | 201 |
| abstract_inverted_index.dimensional | 175, 215 |
| abstract_inverted_index.extensively | 108 |
| abstract_inverted_index.implemented | 161 |
| abstract_inverted_index.informative | 75 |
| abstract_inverted_index.literature. | 198 |
| abstract_inverted_index.scalability | 165, 209 |
| abstract_inverted_index.considerably | 230 |
| abstract_inverted_index.construction | 40 |
| abstract_inverted_index.minimization | 147 |
| abstract_inverted_index.optimization | 148 |
| abstract_inverted_index.decomposition | 150 |
| abstract_inverted_index.effectiveness | 111 |
| abstract_inverted_index.incrementally | 100 |
| abstract_inverted_index.triangulation | 43 |
| abstract_inverted_index.linear/nonlinear | 171 |
| abstract_inverted_index.separable/non-separable | 172 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5076003398 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.80766836 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |