Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers
Determining whether an imprecise dataset is imbalanced is not immediate. The vagueness in the data causes that the prior probabilities of the classes are not precisely known, and therefore the degree of imbalance can also be uncertain. In this paper we propose suitable extensions of different resamp...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2012-04-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25867972.pdf |
| _version_ | 1818475545254428672 |
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| author | AnaM. Palacios Luciano Sánchez Inés Couso |
| author_facet | AnaM. Palacios Luciano Sánchez Inés Couso |
| author_sort | AnaM. Palacios |
| collection | DOAJ |
| description | Determining whether an imprecise dataset is imbalanced is not immediate. The vagueness in the data causes that the prior probabilities of the classes are not precisely known, and therefore the degree of imbalance can also be uncertain. In this paper we propose suitable extensions of different resampling algorithms that can be applied to interval valued, multi-labelled data. By means of these extended preprocessing algorithms, certain classification systems designed for minimizing the fraction of misclassifications are able to produce knowledge bases that are also adequate under common metrics for imbalanced classification. |
| first_indexed | 2024-12-10T09:13:53Z |
| format | Article |
| id | doaj.art-db2418eb74ed4747993ad2621acd5610 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-10T09:13:53Z |
| publishDate | 2012-04-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-db2418eb74ed4747993ad2621acd56102022-12-22T01:54:56ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832012-04-015210.1080/18756891.2012.685292Equalizing imbalanced imprecise datasets for genetic fuzzy classifiersAnaM. PalaciosLuciano SánchezInés CousoDetermining whether an imprecise dataset is imbalanced is not immediate. The vagueness in the data causes that the prior probabilities of the classes are not precisely known, and therefore the degree of imbalance can also be uncertain. In this paper we propose suitable extensions of different resampling algorithms that can be applied to interval valued, multi-labelled data. By means of these extended preprocessing algorithms, certain classification systems designed for minimizing the fraction of misclassifications are able to produce knowledge bases that are also adequate under common metrics for imbalanced classification.https://www.atlantis-press.com/article/25867972.pdfGenetic Fuzzy SystemsInterval Valued DataImbalanced ClassificationLow Quality Data |
| spellingShingle | AnaM. Palacios Luciano Sánchez Inés Couso Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers International Journal of Computational Intelligence Systems Genetic Fuzzy Systems Interval Valued Data Imbalanced Classification Low Quality Data |
| title | Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers |
| title_full | Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers |
| title_fullStr | Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers |
| title_full_unstemmed | Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers |
| title_short | Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers |
| title_sort | equalizing imbalanced imprecise datasets for genetic fuzzy classifiers |
| topic | Genetic Fuzzy Systems Interval Valued Data Imbalanced Classification Low Quality Data |
| url | https://www.atlantis-press.com/article/25867972.pdf |
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