Bi-objective evolutionary hyper-heuristics in automated machine learning for text classification tasks
This paper proposes an evolutionary model based on hyper-heuristics to automate the selection of classification methods for text datasets under a bi-objective approach. The model has three nested levels. At the first level, individual methods classify datasets, recording two performances: the number...
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| Vydáno v: | Swarm and evolutionary computation Ročník 98; s. 102073 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.10.2025
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| Témata: | |
| ISSN: | 2210-6502 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | This paper proposes an evolutionary model based on hyper-heuristics to automate the selection of classification methods for text datasets under a bi-objective approach. The model has three nested levels. At the first level, individual methods classify datasets, recording two performances: the number of misclassifications and computational time, which are often in conflict. At the second level, hyper-heuristics, as a set of rules of the form if→then, select classification methods for datasets based on 16 meta-features representing the data distribution. The fitness for a hyper-heuristic is evaluated on a training group of datasets by aggregating the two low-level performances of the chosen methods. At the third level, the multi-objective evolutionary algorithm Strength Pareto Evolutionary Algorithm 2 evolves hyper-heuristic populations considering the bi-objective of minimizing the two aggregated performances. The result is a Pareto-approximated front of hyper-heuristics, which offers a range of solutions from computationally efficient to high classification performance. Finally, the model evaluates the front with an independent test group of datasets and selects those hyper-heuristics that are not dominated. We evaluated the resulting fronts through extensive experiments, measuring several quality indicators. We compare the model’s fronts with a front baseline consisting of non-dominated individual classification methods and four state-of-the-art automated machine learning tools (AutoKeras, AutoGluon, H2O, and TPOT). The proposed model yields larger, more diverse Pareto-approximated fronts that outperform the baseline front, allowing solution selection based on available resources and trade-offs between performance and cost.
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•An evolutionary model based on HHs is presented.•The HHs select the most adequate classification methods for text datasets.•The selection is bi-objective: classification performance and computational time.•SPEA2 is used to produce PAFs of HHs using the two objectives.•The model’s PAFs outperforms the baseline considering different quality indicators. |
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| ISSN: | 2210-6502 |
| DOI: | 10.1016/j.swevo.2025.102073 |