A Greedy Method for Constructing Minimal Multiconlitron Article Swipe
Qiangkui Leng
,
Y. Liu
,
Xu Zhao
,
L. Zhang
,
Yin Qin
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2957516
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2957516
Multiconlitron is a general theoretical framework for constructing piecewise linear classifier. However, it contains a relatively large number of linear functions, resulting in complicated model structure and poor generalization ability. Learning to prune redundant or excessive components may be a very necessary progression. We propose a novel greedy method, i.e., greedy support multiconlitron algorithm (GreSMA) to simplify the multiconlitron. In GreSMA, a procedure of greedy selection is first used. It generates the initial linear boundaries, each of which can separate maximum number of training samples under the current iteration. In this way, a minimal set of decision functions is established. In the second stage of GreSMA, a procedure of boundary adjustment is designed to retrain the classification boundary between convex hulls of local subsets, instead of individual samples. Thus, the adjusted boundary will fit the data more closely. Experiments on both synthetic and real-world datasets show that GreSMA can produce minimal multiconlitron with better performance. It meets the criteria of “Occam’s razor”, since simpler model can help prevent over-fitting and improve the generalization ability. More significantly, the proposed method does not contain parameters that depend on the datasets or make assumptions of the underlying statistical distributions of the samples. Therefore, it should be regarded as an attractive advancement of piecewise linear learning in the general framework of multiconlitron.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2019.2957516
- https://ieeexplore.ieee.org/ielx7/6287639/8600701/08922614.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2993928980
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2993928980Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2019.2957516Digital Object Identifier
- Title
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A Greedy Method for Constructing Minimal MulticonlitronWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Qiangkui Leng, Y. Liu, Xu Zhao, L. Zhang, Yin QinList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2019.2957516Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/8600701/08922614.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/8600701/08922614.pdfDirect OA link when available
- Concepts
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Computer science, Greedy algorithm, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2022: 1Per-year citation counts (last 5 years)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.hulls | 120 |
| abstract_inverted_index.i.e., | 49 |
| abstract_inverted_index.large | 16 |
| abstract_inverted_index.local | 122 |
| abstract_inverted_index.meets | 156 |
| abstract_inverted_index.model | 24, 164 |
| abstract_inverted_index.novel | 46 |
| abstract_inverted_index.prune | 32 |
| abstract_inverted_index.since | 162 |
| abstract_inverted_index.stage | 103 |
| abstract_inverted_index.under | 85 |
| abstract_inverted_index.used. | 68 |
| abstract_inverted_index.which | 77 |
| abstract_inverted_index.GreSMA | 147 |
| abstract_inverted_index.better | 153 |
| abstract_inverted_index.convex | 119 |
| abstract_inverted_index.depend | 184 |
| abstract_inverted_index.greedy | 47, 50, 64 |
| abstract_inverted_index.linear | 9, 19, 73, 210 |
| abstract_inverted_index.method | 178 |
| abstract_inverted_index.number | 17, 81 |
| abstract_inverted_index.second | 102 |
| abstract_inverted_index.should | 201 |
| abstract_inverted_index.GreSMA, | 60, 105 |
| abstract_inverted_index.between | 118 |
| abstract_inverted_index.contain | 181 |
| abstract_inverted_index.current | 87 |
| abstract_inverted_index.general | 3, 214 |
| abstract_inverted_index.improve | 170 |
| abstract_inverted_index.initial | 72 |
| abstract_inverted_index.instead | 124 |
| abstract_inverted_index.maximum | 80 |
| abstract_inverted_index.method, | 48 |
| abstract_inverted_index.minimal | 93, 150 |
| abstract_inverted_index.prevent | 167 |
| abstract_inverted_index.produce | 149 |
| abstract_inverted_index.propose | 44 |
| abstract_inverted_index.retrain | 114 |
| abstract_inverted_index.samples | 84 |
| abstract_inverted_index.simpler | 163 |
| abstract_inverted_index.support | 51 |
| abstract_inverted_index.(GreSMA) | 54 |
| abstract_inverted_index.However, | 11 |
| abstract_inverted_index.Learning | 30 |
| abstract_inverted_index.ability. | 29, 173 |
| abstract_inverted_index.adjusted | 130 |
| abstract_inverted_index.boundary | 109, 117, 131 |
| abstract_inverted_index.closely. | 137 |
| abstract_inverted_index.contains | 13 |
| abstract_inverted_index.criteria | 158 |
| abstract_inverted_index.datasets | 144, 187 |
| abstract_inverted_index.decision | 96 |
| abstract_inverted_index.designed | 112 |
| abstract_inverted_index.learning | 211 |
| abstract_inverted_index.proposed | 177 |
| abstract_inverted_index.regarded | 203 |
| abstract_inverted_index.samples. | 127, 198 |
| abstract_inverted_index.separate | 79 |
| abstract_inverted_index.simplify | 56 |
| abstract_inverted_index.subsets, | 123 |
| abstract_inverted_index.training | 83 |
| abstract_inverted_index.algorithm | 53 |
| abstract_inverted_index.excessive | 35 |
| abstract_inverted_index.framework | 5, 215 |
| abstract_inverted_index.functions | 97 |
| abstract_inverted_index.generates | 70 |
| abstract_inverted_index.necessary | 41 |
| abstract_inverted_index.piecewise | 8, 209 |
| abstract_inverted_index.procedure | 62, 107 |
| abstract_inverted_index.redundant | 33 |
| abstract_inverted_index.resulting | 21 |
| abstract_inverted_index.selection | 65 |
| abstract_inverted_index.structure | 25 |
| abstract_inverted_index.synthetic | 141 |
| abstract_inverted_index.Therefore, | 199 |
| abstract_inverted_index.adjustment | 110 |
| abstract_inverted_index.attractive | 206 |
| abstract_inverted_index.components | 36 |
| abstract_inverted_index.functions, | 20 |
| abstract_inverted_index.individual | 126 |
| abstract_inverted_index.iteration. | 88 |
| abstract_inverted_index.parameters | 182 |
| abstract_inverted_index.real-world | 143 |
| abstract_inverted_index.relatively | 15 |
| abstract_inverted_index.underlying | 193 |
| abstract_inverted_index.Experiments | 138 |
| abstract_inverted_index.advancement | 207 |
| abstract_inverted_index.assumptions | 190 |
| abstract_inverted_index.boundaries, | 74 |
| abstract_inverted_index.classifier. | 10 |
| abstract_inverted_index.complicated | 23 |
| abstract_inverted_index.statistical | 194 |
| abstract_inverted_index.theoretical | 4 |
| abstract_inverted_index.constructing | 7 |
| abstract_inverted_index.established. | 99 |
| abstract_inverted_index.over-fitting | 168 |
| abstract_inverted_index.performance. | 154 |
| abstract_inverted_index.progression. | 42 |
| abstract_inverted_index.distributions | 195 |
| abstract_inverted_index.Multiconlitron | 0 |
| abstract_inverted_index.classification | 116 |
| abstract_inverted_index.generalization | 28, 172 |
| abstract_inverted_index.multiconlitron | 52, 151 |
| abstract_inverted_index.significantly, | 175 |
| abstract_inverted_index.multiconlitron. | 58, 217 |
| abstract_inverted_index.razor”, | 161 |
| abstract_inverted_index.“Occam’s | 160 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile.value | 0.47204003 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |