Classification is among the core tasks in machine learning. Existing classification algorithms are typically based on assumption of at least roughly balanced data classes. When performing involving imbalanced data, such classifiers ignore minority consideration overall accuracy. The performance traditional distribution insufficient because minority-class samples often more important than others...

0950-7051/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.knosys.2011.06.013 ⇑ Corresponding author. E-mail addresses: [email protected] (V. García), s [email protected] (R.A. Mollineda). The present paper investigates the influence of both the imbalance ratio and the classifier on the performance of several resampling strategies to deal with imbalanced data sets. The study focuses on evaluat...

Lung nodule detection is a class imbalanced problem because nodules are found with much lower frequency than nonnodules. In the class imbalanced problem, conventional classifiers tend to be overwhelmed by the majority class and ignore the minority class. We therefore propose cascaded convolutional neural networks to cope with the class imbalanced problem. In the proposed approach, cascaded conv...