Résumé
Feature selection and taking into account dynamic environments are two important aspects of modern data analysis and machine learning. In particular, performing feature selection on datasets where the latest instances contain more features than the initial ones is a problem that may be encountered in many application areas where new sensors are acquired. This paper proposes a method for incremental feature selection with rankings combining the information extracted before and after the introduction of new features, even when the number of instances that include these new features is small. Results on three real-world datasets show that using the ranking of features on the original, smaller-dimensional dataset improves the feature selection results performed on the new, larger-dimensional dataset.
langue originale | Anglais |
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titre | Proceedings of the International Joint Conference on Neural Networks |
Editeur | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1-8 |
Nombre de pages | 8 |
Volume | 2015-September |
ISBN (imprimé) | 9781479919604 |
Les DOIs | |
Etat de la publication | Publié - 28 sept. 2015 |
Evénement | International Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Irlande Durée: 12 juil. 2015 → 17 juil. 2015 |
Une conférence
Une conférence | International Joint Conference on Neural Networks, IJCNN 2015 |
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Pays/Territoire | Irlande |
La ville | Killarney |
période | 12/07/15 → 17/07/15 |