Denying Evolution Resampling: An Improved Method for Feature Selection on Imbalanced Data Article Swipe
Li Quan
,
Tao Gong
,
Kaida Jiang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12153212
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12153212
Imbalanced data classification is an important problem in the field of computer science. Traditional classification algorithms often experience a decrease in accuracy when the data distribution is uneven. Therefore, measures need to be taken to improve the balance of the dataset and enhance the classification accuracy of the model. We have designed a data resampling method to improve the accuracy of classification detection. This method relies on the negative selection process to constrain the data evolution process. By combining the CRITIC method with regression coefficients, we establish crossover selection probabilities for elite genes to achieve an evolutionary resampling process. Based on independent weights, the feature analysis improves by 3%. We evaluated the resampled results on publicly available datasets using traditional logistic regression with cross-validation. Compared to the other resampling models, the F1 score performance of the logistic regression five-fold cross-validation is more stable than the other methods using the two sampling results of the proposed method. The effectiveness of the proposed method is verified based on F1 score evaluation results.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics12153212
- https://www.mdpi.com/2079-9292/12/15/3212/pdf?version=1690275783
- OA Status
- gold
- Cited By
- 3
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385319625
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385319625Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics12153212Digital Object Identifier
- Title
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Denying Evolution Resampling: An Improved Method for Feature Selection on Imbalanced DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-25Full publication date if available
- Authors
-
Li Quan, Tao Gong, Kaida JiangList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics12153212Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/12/15/3212/pdf?version=1690275783Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2079-9292/12/15/3212/pdf?version=1690275783Direct OA link when available
- Concepts
-
Resampling, Feature selection, Computer science, Logistic regression, Artificial intelligence, Data mining, Crossover, Pattern recognition (psychology), Feature (linguistics), Selection (genetic algorithm), Regression, Machine learning, Statistics, Mathematics, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
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45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3172695087, https://openalex.org/W4381250378, https://openalex.org/W4210242534, https://openalex.org/W4321380148, https://openalex.org/W4315815629, https://openalex.org/W4321021182, https://openalex.org/W6838882900, https://openalex.org/W3164112495, https://openalex.org/W3216305262, https://openalex.org/W6796253837, https://openalex.org/W4312434087, https://openalex.org/W2157271994, https://openalex.org/W2955699134, https://openalex.org/W3087306757, https://openalex.org/W3152794541, https://openalex.org/W2010199086, https://openalex.org/W2102798330, https://openalex.org/W4310145190, https://openalex.org/W4225407267, https://openalex.org/W2955505172, https://openalex.org/W3211938211, https://openalex.org/W2086023346, https://openalex.org/W2072651823, https://openalex.org/W4253953323, https://openalex.org/W4200567002, https://openalex.org/W3214066484, https://openalex.org/W2040895929, https://openalex.org/W2098920923, https://openalex.org/W3080558755, https://openalex.org/W4214572276, https://openalex.org/W4281396973, https://openalex.org/W4365450399, https://openalex.org/W2907577147, https://openalex.org/W3043187969, https://openalex.org/W6852532729, https://openalex.org/W2964105864, https://openalex.org/W4313544751, https://openalex.org/W2066495914, https://openalex.org/W3170148327, https://openalex.org/W2756359217, https://openalex.org/W2217007515, https://openalex.org/W4312679298, https://openalex.org/W3164273471, https://openalex.org/W4282934804, https://openalex.org/W4367849720 |
| referenced_works_count | 45 |
| abstract_inverted_index.a | 18, 52 |
| abstract_inverted_index.By | 77 |
| abstract_inverted_index.F1 | 131, 166 |
| abstract_inverted_index.We | 49, 109 |
| abstract_inverted_index.an | 4, 95 |
| abstract_inverted_index.be | 32 |
| abstract_inverted_index.by | 107 |
| abstract_inverted_index.in | 7, 20 |
| abstract_inverted_index.is | 3, 26, 140, 162 |
| abstract_inverted_index.of | 10, 38, 46, 60, 134, 152, 158 |
| abstract_inverted_index.on | 66, 100, 114, 165 |
| abstract_inverted_index.to | 31, 34, 56, 71, 93, 125 |
| abstract_inverted_index.we | 85 |
| abstract_inverted_index.3%. | 108 |
| abstract_inverted_index.The | 156 |
| abstract_inverted_index.and | 41 |
| abstract_inverted_index.for | 90 |
| abstract_inverted_index.the | 8, 23, 36, 39, 43, 47, 58, 67, 73, 79, 103, 111, 126, 130, 135, 144, 148, 153, 159 |
| abstract_inverted_index.two | 149 |
| abstract_inverted_index.This | 63 |
| abstract_inverted_index.data | 1, 24, 53, 74 |
| abstract_inverted_index.have | 50 |
| abstract_inverted_index.more | 141 |
| abstract_inverted_index.need | 30 |
| abstract_inverted_index.than | 143 |
| abstract_inverted_index.when | 22 |
| abstract_inverted_index.with | 82, 122 |
| abstract_inverted_index.Based | 99 |
| abstract_inverted_index.based | 164 |
| abstract_inverted_index.elite | 91 |
| abstract_inverted_index.field | 9 |
| abstract_inverted_index.genes | 92 |
| abstract_inverted_index.often | 16 |
| abstract_inverted_index.other | 127, 145 |
| abstract_inverted_index.score | 132, 167 |
| abstract_inverted_index.taken | 33 |
| abstract_inverted_index.using | 118, 147 |
| abstract_inverted_index.CRITIC | 80 |
| abstract_inverted_index.method | 55, 64, 81, 161 |
| abstract_inverted_index.model. | 48 |
| abstract_inverted_index.relies | 65 |
| abstract_inverted_index.stable | 142 |
| abstract_inverted_index.achieve | 94 |
| abstract_inverted_index.balance | 37 |
| abstract_inverted_index.dataset | 40 |
| abstract_inverted_index.enhance | 42 |
| abstract_inverted_index.feature | 104 |
| abstract_inverted_index.improve | 35, 57 |
| abstract_inverted_index.method. | 155 |
| abstract_inverted_index.methods | 146 |
| abstract_inverted_index.models, | 129 |
| abstract_inverted_index.problem | 6 |
| abstract_inverted_index.process | 70 |
| abstract_inverted_index.results | 113, 151 |
| abstract_inverted_index.uneven. | 27 |
| abstract_inverted_index.Compared | 124 |
| abstract_inverted_index.accuracy | 21, 45, 59 |
| abstract_inverted_index.analysis | 105 |
| abstract_inverted_index.computer | 11 |
| abstract_inverted_index.datasets | 117 |
| abstract_inverted_index.decrease | 19 |
| abstract_inverted_index.designed | 51 |
| abstract_inverted_index.improves | 106 |
| abstract_inverted_index.logistic | 120, 136 |
| abstract_inverted_index.measures | 29 |
| abstract_inverted_index.negative | 68 |
| abstract_inverted_index.process. | 76, 98 |
| abstract_inverted_index.proposed | 154, 160 |
| abstract_inverted_index.publicly | 115 |
| abstract_inverted_index.results. | 169 |
| abstract_inverted_index.sampling | 150 |
| abstract_inverted_index.science. | 12 |
| abstract_inverted_index.verified | 163 |
| abstract_inverted_index.weights, | 102 |
| abstract_inverted_index.available | 116 |
| abstract_inverted_index.combining | 78 |
| abstract_inverted_index.constrain | 72 |
| abstract_inverted_index.crossover | 87 |
| abstract_inverted_index.establish | 86 |
| abstract_inverted_index.evaluated | 110 |
| abstract_inverted_index.evolution | 75 |
| abstract_inverted_index.five-fold | 138 |
| abstract_inverted_index.important | 5 |
| abstract_inverted_index.resampled | 112 |
| abstract_inverted_index.selection | 69, 88 |
| abstract_inverted_index.Imbalanced | 0 |
| abstract_inverted_index.Therefore, | 28 |
| abstract_inverted_index.algorithms | 15 |
| abstract_inverted_index.detection. | 62 |
| abstract_inverted_index.evaluation | 168 |
| abstract_inverted_index.experience | 17 |
| abstract_inverted_index.regression | 83, 121, 137 |
| abstract_inverted_index.resampling | 54, 97, 128 |
| abstract_inverted_index.Traditional | 13 |
| abstract_inverted_index.independent | 101 |
| abstract_inverted_index.performance | 133 |
| abstract_inverted_index.traditional | 119 |
| abstract_inverted_index.distribution | 25 |
| abstract_inverted_index.evolutionary | 96 |
| abstract_inverted_index.coefficients, | 84 |
| abstract_inverted_index.effectiveness | 157 |
| abstract_inverted_index.probabilities | 89 |
| abstract_inverted_index.classification | 2, 14, 44, 61 |
| abstract_inverted_index.cross-validation | 139 |
| abstract_inverted_index.cross-validation. | 123 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5088814076 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I181326427 |
| citation_normalized_percentile.value | 0.73268679 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |