Text Sampling and Re-Sampling for Imbalanced Authorship Identification Cases

Authorship identification can be seen as a single-label multi-class text categorization problem. Very often, there are extremely few training texts at least for some of the candidate authors. In this paper, we present methods to handle imbalanced multi-class textual datasets. The main idea is to segment the training texts into sub-samples according to the size of the class. Hence, minority classes can be segmented into many short samples and majority classes into less and longer samples. Moreover, we explore text re-sampling in order to construct a training set according to a desirable distribution over the classes. Essentially, text re-sampling can be viewed as providing new synthetic data that increase the training size of a class. Based on a corpus of newswire stories in English we present authorship identification experiments on various multi-class imbalanced cases.