Data- and Algorithm-Hybrid Approach for Imbalanced Data Problems in Deep Neural Network
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[1] Songqing Yue,et al. Imbalanced Malware Images Classification: a CNN based Approach , 2017, ArXiv.
[2] José Carlos Príncipe,et al. Nearest Neighbor Distributions for imbalanced classification , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).
[3] B. S. Manjunath,et al. Malware images: visualization and automatic classification , 2011, VizSec '11.
[4] Tomasz Maciejewski,et al. Local neighbourhood extension of SMOTE for mining imbalanced data , 2011, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).
[5] Chumphol Bunkhumpornpat,et al. Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem , 2009, PAKDD.
[6] Hui Han,et al. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.
[7] Gary M. Weiss. Mining with rarity: a unifying framework , 2004, SKDD.
[8] Gustavo E. A. P. A. Batista,et al. A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.
[9] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..