Combining "Random Subspace" Approach with smote Oversampling for Imbalanced Data Classification

• Pawel Ksieniewicz ^[1]
  1. [1] Wrocław University of Technology

     Wrocław University of Technology

     Breslavia, Polonia

     
• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 660-673
• Idioma: inglés
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• Resumen

  • Following work tries to utilize a hybrid approach of combining "Random Subspace" method and smote oversampling to solve a problem of imbalanced data classification. Paper contains a proposition of the ensemble diversified using Random Subspace approach, trained with a set oversampled in the context of each reduced subset of features. Algorithm was evaluated on the basis of the computer experiments carried out on the benchmark datasets and three different base classifiers.