A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data.

ABSTRACT: Class imbalance presents a major hurdle in the application of classification methods. A commonly taken approach is to learn ensembles of classifiers using rebalanced data. Examples include bootstrap averaging (bagging) combined with either undersampling or oversampling of the minority class examples. However, rebalancing methods entail asymmetric changes to the examples of different classes, which in turn can introduce their own biases. Furthermore, these methods often require specifying the performance measure of interest a priori, i.e., before learning. An alternative is to employ the threshold moving technique, which applies a threshold to the continuous output of a model, offering the possibility to adapt to a performance measure a posteriori, i.e., a plug-in metho

SUBMITTER: Collell G 

PROVIDER: S-EPMC5750819 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature