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| Titel: |
Prototype induction and attribute selection via evolutionary algorithms |
| Auteur: |
Xavier Llorà Josep M. Garrell |
| Verschenen in: |
Intelligent data analysis |
| Paginering: | Jaargang 7 (2003) nr. 3 pagina's 193-208 |
| Jaar: |
2003-08-05 |
| Inhoud: |
This paper addresses the issue of reducing the storage requirements on instance-based learning algorithms. Algorithms proposed by other researches use heuristics to prune instances of the training set or modify the instances themselves to achieve a reduced set of instances. This paper presents an alternative way. The presented approach proposes to induce a reduced set of prototypes (partially-defined instances) with evolutionary algorithms. Experiments were performed with GALE, a fine-grained parallel evolutionary algorithm, and other well-known reduction techniques on several data sets. Results suggest that GALE is competitive and robust for inducing sets of partially-defined instances. Moreover, it achieves better reduction rates in storage requirements without losses in generalization accuracy. Simultaneously, if the partially-defined instances induced by GALE are post-processed, results can also be used for attribute selection. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
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