Dimensionality Reduction for Hierarchical Multi-Label Classification: A Systematic Mapping Study
Hierarchical multi-label classification problems typically deal with datasets with many attributes and labels, which can negatively impact the classifier performance. The application of dimensionality reduction methods can significantly improve the performance of classifiers. Dimensionality reductio...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Graz University of Technology
2024-01-01
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| Series: | Journal of Universal Computer Science |
| Subjects: | |
| Online Access: | https://lib.jucs.org/article/91309/download/pdf/ |
| _version_ | 1797338702384463872 |
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| author | Raimundo Osvaldo Vieira Helyane Bronoski Borges |
| author_facet | Raimundo Osvaldo Vieira Helyane Bronoski Borges |
| author_sort | Raimundo Osvaldo Vieira |
| collection | DOAJ |
| description | Hierarchical multi-label classification problems typically deal with datasets with many attributes and labels, which can negatively impact the classifier performance. The application of dimensionality reduction methods can significantly improve the performance of classifiers. Dimensionality reduction can be performed by feature extraction or feature selection, according to the problem domain and datasets characteristics. This work carried out a systematic literature mapping to identify the approaches and techniques of dimensionality reduction that have been used in hierarchical multi-label classification tasks. Searches were performed on 7 important databases for the Computer Science field. From a list of 184 retrieved papers, 12 were selected for analysis, from which it was possible to determine a general overview of studies conducted from 2010 to 2022. It was identified that feature selection was the most frequent reduction method, with filter approach standing out. In addition, it was detected that most of the works used tree hierarchical structure. As its main outcome, this paper presents the state of the art of dimensionality reduction problem for hierarchical multi-label classification, indicating trends and research issues in the field. |
| first_indexed | 2024-03-08T09:35:04Z |
| format | Article |
| id | doaj.art-a5eae89d20a84a5fbe70b4b6bb53f0f9 |
| institution | Directory Open Access Journal |
| issn | 0948-6968 |
| language | English |
| last_indexed | 2024-03-08T09:35:04Z |
| publishDate | 2024-01-01 |
| publisher | Graz University of Technology |
| record_format | Article |
| series | Journal of Universal Computer Science |
| spelling | doaj.art-a5eae89d20a84a5fbe70b4b6bb53f0f92024-01-30T10:45:31ZengGraz University of TechnologyJournal of Universal Computer Science0948-69682024-01-0130113015010.3897/jucs.9130991309Dimensionality Reduction for Hierarchical Multi-Label Classification: A Systematic Mapping StudyRaimundo Osvaldo Vieira0Helyane Bronoski Borges1Universidade Tecnológica Federal do ParanáUniversidade Tecnológica Federal do ParanáHierarchical multi-label classification problems typically deal with datasets with many attributes and labels, which can negatively impact the classifier performance. The application of dimensionality reduction methods can significantly improve the performance of classifiers. Dimensionality reduction can be performed by feature extraction or feature selection, according to the problem domain and datasets characteristics. This work carried out a systematic literature mapping to identify the approaches and techniques of dimensionality reduction that have been used in hierarchical multi-label classification tasks. Searches were performed on 7 important databases for the Computer Science field. From a list of 184 retrieved papers, 12 were selected for analysis, from which it was possible to determine a general overview of studies conducted from 2010 to 2022. It was identified that feature selection was the most frequent reduction method, with filter approach standing out. In addition, it was detected that most of the works used tree hierarchical structure. As its main outcome, this paper presents the state of the art of dimensionality reduction problem for hierarchical multi-label classification, indicating trends and research issues in the field.https://lib.jucs.org/article/91309/download/pdf/Hierarchical Multi-label ClassificationDimension |
| spellingShingle | Raimundo Osvaldo Vieira Helyane Bronoski Borges Dimensionality Reduction for Hierarchical Multi-Label Classification: A Systematic Mapping Study Journal of Universal Computer Science Hierarchical Multi-label Classification Dimension |
| title | Dimensionality Reduction for Hierarchical Multi-Label Classification: A Systematic Mapping Study |
| title_full | Dimensionality Reduction for Hierarchical Multi-Label Classification: A Systematic Mapping Study |
| title_fullStr | Dimensionality Reduction for Hierarchical Multi-Label Classification: A Systematic Mapping Study |
| title_full_unstemmed | Dimensionality Reduction for Hierarchical Multi-Label Classification: A Systematic Mapping Study |
| title_short | Dimensionality Reduction for Hierarchical Multi-Label Classification: A Systematic Mapping Study |
| title_sort | dimensionality reduction for hierarchical multi label classification a systematic mapping study |
| topic | Hierarchical Multi-label Classification Dimension |
| url | https://lib.jucs.org/article/91309/download/pdf/ |
| work_keys_str_mv | AT raimundoosvaldovieira dimensionalityreductionforhierarchicalmultilabelclassificationasystematicmappingstudy AT helyanebronoskiborges dimensionalityreductionforhierarchicalmultilabelclassificationasystematicmappingstudy |