Título
Synthetic Oversampling of Instances Using Clustering
Autor
Atlántida Irene Sánchez Vivar
Eduardo Francisco Morales Manzanares
Jesús Antonio González Bernal
Nivel de Acceso
Acceso Abierto
Materias
Imbalanced datasets - (IMBALANCED DATASETS) Oversampling - (OVERSAMPLING) Cluster-based oversampling - (CLUSTER-BASED OVERSAMPLING) Jittering - (JITTERING) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)
Resumen o descripción
Imbalanced data sets, in the class distribution, is common to many real world applications. As many classifiers tend to degrade their performance over the minority class, several approaches have been proposed to deal with this problem. In this paper, we propose two new cluster-based oversampling methods, SOI-C and SOI-CJ. The proposed methods create clusters from the minority class instances and generate synthetic instances inside those clusters. In contrast with other oversampling methods, the proposed approaches avoid creating new instances in majority class regions. They are more robust to noisy examples (the number of new instances generated per cluster is proportional to the cluster's size). The clusters are automatically generated. Our new methods do not need tuning parameters, and they can deal both with numerical and nominal attributes. The two methods were tested with twenty artificial datasets and twenty three datasets from the UCI Machine Learning repository. For our experiments, we used six classifiers and results were evaluated with TPR, precision, F-measure, and AUC measures, which are more suitable for class imbalanced datasets. We performed ANOVA and paired t-tests to show that the proposed methods are competitive and in many cases significantly better than the rest of the oversampling methods used during the comparison.
Editor
World Scientific Publishing Company
Fecha de publicación
2013
Tipo de publicación
Artículo
Versión de la publicación
Versión aceptada
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Estudiantes
Investigadores
Público en general
Sugerencia de citación
Sánchez, A., et al., (2013). Synthetic Oversampling of Instances Using Clustering, International Journal on Artificial Intelligence Tools, Vol. 22 (2): 1-22
Repositorio Orígen
Repositorio Institucional del INAOE
Descargas
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