A Cross-project Defect Prediction Model Using Feature Transfer and Ensemble Learning Article Swipe
Fuping Zeng
,
Wanting Lin
,
Ying Xing
,
Lu Sun
,
Bin Yang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.17559/tv-20220421110027
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.17559/tv-20220421110027
Cross-project defect prediction (CPDP) trains the prediction models with existing data from other projects (the source projects) and uses the trained model to predict the target projects. To solve two major problems in CPDP, namely, variability in data distribution and class imbalance, in this paper we raise a CPDP model combining feature transfer and ensemble learning, with two stages of feature transfer and the classification. The feature transfer method is based on Pearson correlation coefficient, which reduces the dimension of feature space and the difference of feature distribution between items. The class imbalance is solved by SMOTE and Voting on both algorithm and data levels. The experimental results on 20 source-target projects show that our method can yield significant improvement on CPDP.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17559/tv-20220421110027
- https://hrcak.srce.hr/file/404757
- OA Status
- gold
- Cited By
- 1
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- 28
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- 10
- OpenAlex ID
- https://openalex.org/W4283271185
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https://openalex.org/W4283271185Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.17559/tv-20220421110027Digital Object Identifier
- Title
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A Cross-project Defect Prediction Model Using Feature Transfer and Ensemble LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-06-22Full publication date if available
- Authors
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Fuping Zeng, Wanting Lin, Ying Xing, Lu Sun, Bin YangList of authors in order
- Landing page
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https://doi.org/10.17559/tv-20220421110027Publisher landing page
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https://hrcak.srce.hr/file/404757Direct 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://hrcak.srce.hr/file/404757Direct OA link when available
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Feature (linguistics), Transfer of learning, Ensemble learning, Computer science, Ensemble forecasting, Artificial intelligence, Transfer (computing), Machine learning, Parallel computing, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.both | 100 |
| abstract_inverted_index.data | 10, 37, 103 |
| abstract_inverted_index.from | 11 |
| abstract_inverted_index.show | 112 |
| abstract_inverted_index.that | 113 |
| abstract_inverted_index.this | 43 |
| abstract_inverted_index.uses | 18 |
| abstract_inverted_index.with | 8, 56 |
| abstract_inverted_index.CPDP, | 33 |
| abstract_inverted_index.CPDP. | 121 |
| abstract_inverted_index.SMOTE | 96 |
| abstract_inverted_index.based | 70 |
| abstract_inverted_index.class | 40, 91 |
| abstract_inverted_index.major | 30 |
| abstract_inverted_index.model | 21, 49 |
| abstract_inverted_index.other | 12 |
| abstract_inverted_index.paper | 44 |
| abstract_inverted_index.raise | 46 |
| abstract_inverted_index.solve | 28 |
| abstract_inverted_index.space | 81 |
| abstract_inverted_index.which | 75 |
| abstract_inverted_index.yield | 117 |
| abstract_inverted_index.(CPDP) | 3 |
| abstract_inverted_index.Voting | 98 |
| abstract_inverted_index.defect | 1 |
| abstract_inverted_index.items. | 89 |
| abstract_inverted_index.method | 68, 115 |
| abstract_inverted_index.models | 7 |
| abstract_inverted_index.solved | 94 |
| abstract_inverted_index.source | 15 |
| abstract_inverted_index.stages | 58 |
| abstract_inverted_index.target | 25 |
| abstract_inverted_index.trains | 4 |
| abstract_inverted_index.Pearson | 72 |
| abstract_inverted_index.between | 88 |
| abstract_inverted_index.feature | 51, 60, 66, 80, 86 |
| abstract_inverted_index.levels. | 104 |
| abstract_inverted_index.namely, | 34 |
| abstract_inverted_index.predict | 23 |
| abstract_inverted_index.reduces | 76 |
| abstract_inverted_index.results | 107 |
| abstract_inverted_index.trained | 20 |
| abstract_inverted_index.ensemble | 54 |
| abstract_inverted_index.existing | 9 |
| abstract_inverted_index.problems | 31 |
| abstract_inverted_index.projects | 13, 111 |
| abstract_inverted_index.transfer | 52, 61, 67 |
| abstract_inverted_index.algorithm | 101 |
| abstract_inverted_index.combining | 50 |
| abstract_inverted_index.dimension | 78 |
| abstract_inverted_index.imbalance | 92 |
| abstract_inverted_index.learning, | 55 |
| abstract_inverted_index.projects) | 16 |
| abstract_inverted_index.projects. | 26 |
| abstract_inverted_index.difference | 84 |
| abstract_inverted_index.imbalance, | 41 |
| abstract_inverted_index.prediction | 2, 6 |
| abstract_inverted_index.correlation | 73 |
| abstract_inverted_index.improvement | 119 |
| abstract_inverted_index.significant | 118 |
| abstract_inverted_index.variability | 35 |
| abstract_inverted_index.coefficient, | 74 |
| abstract_inverted_index.distribution | 38, 87 |
| abstract_inverted_index.experimental | 106 |
| abstract_inverted_index.Cross-project | 0 |
| abstract_inverted_index.source-target | 110 |
| abstract_inverted_index.classification. | 64 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.51060971 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |