RUMC: A Rule-based Classifier Inspired by Evolutionary Methods
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912151885578240 |
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| author | Mokhtari, Melvin |
| author_facet | Mokhtari, Melvin |
| contents | As the field of data analysis grows rapidly due to the large amounts of data being generated, effective data classification has become increasingly important. This paper introduces the RUle Mutation Classifier (RUMC), which represents a significant improvement over the Rule Aggregation ClassifiER (RACER). RUMC uses innovative rule mutation techniques based on evolutionary methods to improve classification accuracy. In tests with forty datasets from OpenML and the UCI Machine Learning Repository, RUMC consistently outperformed twenty other well-known classifiers, demonstrating its ability to uncover valuable insights from complex data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07885 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RUMC: A Rule-based Classifier Inspired by Evolutionary Methods Mokhtari, Melvin Machine Learning Neural and Evolutionary Computing As the field of data analysis grows rapidly due to the large amounts of data being generated, effective data classification has become increasingly important. This paper introduces the RUle Mutation Classifier (RUMC), which represents a significant improvement over the Rule Aggregation ClassifiER (RACER). RUMC uses innovative rule mutation techniques based on evolutionary methods to improve classification accuracy. In tests with forty datasets from OpenML and the UCI Machine Learning Repository, RUMC consistently outperformed twenty other well-known classifiers, demonstrating its ability to uncover valuable insights from complex data. |
| title | RUMC: A Rule-based Classifier Inspired by Evolutionary Methods |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2412.07885 |