Variable selection in Poisson regression model based on chaotic meta-heuristic search algorithm
By determining the most significant variables that are connected to the response variable, Increasing prediction accuracy and processing speed can be achieved through the process of variable selection. Regression modeling has drawn a lot of interest from several scientific domains. One of the most e...
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| Published in: | BIO web of conferences Vol. 97; p. 161 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
EDP Sciences
01-01-2024
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| Online Access: | Get full text |
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| Summary: | By determining the most significant variables that are connected to the response variable, Increasing prediction accuracy and processing speed can be achieved through the process of variable selection. Regression modeling has drawn a lot of interest from several scientific domains. One of the most effective nature-inspired algorithms that has been suggested recently and can be used effectively for variable selection is the Firefly algorithm. The chaotic firefly algorithm is presented in this work to carry out the Poisson regression model's variable selection. A simulation study is carried out to assess how well the suggested strategy performs in terms of variable selection criteria and prediction accuracy. Its effectiveness is also contrasted with alternative approaches. The outcomes demonstrated the effectiveness of our suggested strategies, which beat other widely used approaches. |
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| ISSN: | 2117-4458 2117-4458 |
| DOI: | 10.1051/bioconf/20249700161 |