An Investigation on Topic Maps Based Document Classification with Unbalance Classes

Abstract

Classification of imbalanced data has become a widespread problem due to the fact that the most real world datasets are imbalanced. In a classification task, one of the challenges is to learn the feature-space of classification under class-imbalance setting. The majority classes generally have good representation of features in the learned classification function and the minority classes lack this representation; subsequently, the classification for these classes failed more often. In this paper, authors investigate the task of document classification with topic map based representation of documents under class imbalance setting. In order to measure of topic-map based representation for classification under imbalance data, authors compare three representations: Bag-of-Words, Phrases and Topic terms for three approaches (i) under-sampling, (ii) cost-adjusting, and (iii) cluster based sampling. A series of experiments are carried out and results are reported.

Token-level Adaptive Training for Neural Machine Translation

“…The methods of trying to solve this can be divided into two types. The data-based methods (Baloch and Rafi, 2015;Ofek et al, 2017) make use of over-and undersampling to reduce the imbalance. The algorithmbased methods (Zhou and Liu, 2005;Lin et al, 2017) give extra reward to different classes.…”