Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911883140792320 |
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| author | Nikolikj, Ana Kostovska, Ana Cenikj, Gjorgjina Doerr, Carola Eftimov, Tome |
| author_facet | Nikolikj, Ana Kostovska, Ana Cenikj, Gjorgjina Doerr, Carola Eftimov, Tome |
| contents | This study examines the generalization ability of algorithm performance prediction models across various benchmark suites. Comparing the statistical similarity between the problem collections with the accuracy of performance prediction models that are based on exploratory landscape analysis features, we observe that there is a positive correlation between these two measures. Specifically, when the high-dimensional feature value distributions between training and testing suites lack statistical significance, the model tends to generalize well, in the sense that the testing errors are in the same range as the training errors. Two experiments validate these findings: one involving the standard benchmark suites, the BBOB and CEC collections, and another using five collections of affine combinations of BBOB problem instances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12259 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks Nikolikj, Ana Kostovska, Ana Cenikj, Gjorgjina Doerr, Carola Eftimov, Tome Machine Learning Neural and Evolutionary Computing This study examines the generalization ability of algorithm performance prediction models across various benchmark suites. Comparing the statistical similarity between the problem collections with the accuracy of performance prediction models that are based on exploratory landscape analysis features, we observe that there is a positive correlation between these two measures. Specifically, when the high-dimensional feature value distributions between training and testing suites lack statistical significance, the model tends to generalize well, in the sense that the testing errors are in the same range as the training errors. Two experiments validate these findings: one involving the standard benchmark suites, the BBOB and CEC collections, and another using five collections of affine combinations of BBOB problem instances. |
| title | Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2405.12259 |