EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm.
Missing data is a prevalent problem that requires attention, as most data analysis techniques are unable to handle it. This is particularly critical in Multi-Label Classification (MLC), where only a few studies have investigated missing data in this application domain. MLC differs from Single-Label...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0297147&type=printable |
| _version_ | 1827376841118187520 |
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| author | Antonio Fernando Lavareda Jacob Junior Fabricio Almeida do Carmo Adamo Lima de Santana Ewaldo Eder Carvalho Santana Fabio Manoel Franca Lobato |
| author_facet | Antonio Fernando Lavareda Jacob Junior Fabricio Almeida do Carmo Adamo Lima de Santana Ewaldo Eder Carvalho Santana Fabio Manoel Franca Lobato |
| author_sort | Antonio Fernando Lavareda Jacob Junior |
| collection | DOAJ |
| description | Missing data is a prevalent problem that requires attention, as most data analysis techniques are unable to handle it. This is particularly critical in Multi-Label Classification (MLC), where only a few studies have investigated missing data in this application domain. MLC differs from Single-Label Classification (SLC) by allowing an instance to be associated with multiple classes. Movie classification is a didactic example since it can be "drama" and "bibliography" simultaneously. One of the most usual missing data treatment methods is data imputation, which seeks plausible values to fill in the missing ones. In this scenario, we propose a novel imputation method based on a multi-objective genetic algorithm for optimizing multiple data imputations called Multiple Imputation of Multi-label Classification data with a genetic algorithm, or simply EvoImp. We applied the proposed method in multi-label learning and evaluated its performance using six synthetic databases, considering various missing values distribution scenarios. The method was compared with other state-of-the-art imputation strategies, such as K-Means Imputation (KMI) and weighted K-Nearest Neighbors Imputation (WKNNI). The results proved that the proposed method outperformed the baseline in all the scenarios by achieving the best evaluation measures considering the Exact Match, Accuracy, and Hamming Loss. The superior results were constant in different dataset domains and sizes, demonstrating the EvoImp robustness. Thus, EvoImp represents a feasible solution to missing data treatment for multi-label learning. |
| first_indexed | 2024-03-08T12:29:40Z |
| format | Article |
| id | doaj.art-de743979045a47dc9e3f72e23b035bbd |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-08T12:29:40Z |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-de743979045a47dc9e3f72e23b035bbd2024-01-22T05:31:23ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-01191e029714710.1371/journal.pone.0297147EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm.Antonio Fernando Lavareda Jacob JuniorFabricio Almeida do CarmoAdamo Lima de SantanaEwaldo Eder Carvalho SantanaFabio Manoel Franca LobatoMissing data is a prevalent problem that requires attention, as most data analysis techniques are unable to handle it. This is particularly critical in Multi-Label Classification (MLC), where only a few studies have investigated missing data in this application domain. MLC differs from Single-Label Classification (SLC) by allowing an instance to be associated with multiple classes. Movie classification is a didactic example since it can be "drama" and "bibliography" simultaneously. One of the most usual missing data treatment methods is data imputation, which seeks plausible values to fill in the missing ones. In this scenario, we propose a novel imputation method based on a multi-objective genetic algorithm for optimizing multiple data imputations called Multiple Imputation of Multi-label Classification data with a genetic algorithm, or simply EvoImp. We applied the proposed method in multi-label learning and evaluated its performance using six synthetic databases, considering various missing values distribution scenarios. The method was compared with other state-of-the-art imputation strategies, such as K-Means Imputation (KMI) and weighted K-Nearest Neighbors Imputation (WKNNI). The results proved that the proposed method outperformed the baseline in all the scenarios by achieving the best evaluation measures considering the Exact Match, Accuracy, and Hamming Loss. The superior results were constant in different dataset domains and sizes, demonstrating the EvoImp robustness. Thus, EvoImp represents a feasible solution to missing data treatment for multi-label learning.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0297147&type=printable |
| spellingShingle | Antonio Fernando Lavareda Jacob Junior Fabricio Almeida do Carmo Adamo Lima de Santana Ewaldo Eder Carvalho Santana Fabio Manoel Franca Lobato EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm. PLoS ONE |
| title | EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm. |
| title_full | EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm. |
| title_fullStr | EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm. |
| title_full_unstemmed | EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm. |
| title_short | EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm. |
| title_sort | evoimp multiple imputation of multi label classification data with a genetic algorithm |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0297147&type=printable |
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