Ensemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristics
| Autor: | Eneko Osaba, Pedro Lopez-Garcia, Antonio D. Masegosa, Enrique Onieva, Asier Perallos |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Feature vector 02 engineering and technology Base (topology) Machine learning computer.software_genre Imbalanced data ComputingMethodologies_PATTERNRECOGNITION Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Metaheuristic computer |
| Zdroj: | Applied Intelligence |
| ISSN: | 0924-669X |
| DOI: | 10.1007/s10489-019-01423-6 |
| Popis: | One of the most challenging issues when facing a classification problem is to deal with imbalanced datasets. Recently, ensemble classification techniques have proven to be very successful in addressing this problem. We present an ensemble classification approach based on feature space partitioning for imbalanced classification. A hybrid metaheuristic called GACE is used to optimize the different parameters related to the feature space partitioning. To assess the performance of the proposal, an extensive experimentation over imbalanced and real-world datasets compares different configurations and base classifiers. Its performance is competitive with that of reference techniques in the literature. |
| Databáze: | OpenAIRE |
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