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Using information on class interrelations to improve classification of multiclass imbalanced data: A new resampling algorithm

Janicka Małgorzata, Lango Mateusz, Stefanowski Jerzy

International Journal of Applied Mathematics and Computer Science

2019

Vol. 29, no. 4

769--781

The relations between multiple imbalanced classes can be handled with a specialized approach which evaluates types of examples’ difficulty based on an analysis of the class distribution in the examples’ neighborhood, additionally exploiting information about the similarity of neighboring classes. In this paper, we demonstrate that such an approach can be implemented as a data preprocessing technique and that it can improve the performance of various classifiers on multiclass imbalanced datasets. It has led us to the introduction of a new resampling algorithm, called Similarity Oversampling and Undersampling Preprocessing (SOUP), which resamples examples according to their difficulty. Its experimental evaluation on real and artificial datasets has shown that it is competitive with the most popular decomposition ensembles and better than specialized preprocessing techniques for multi-imbalanced problems.

Tackling the Problem of Class Imbalance in Multi-class Sentiment Classification: An Experimental Study

Lango Mateusz

Foundations of Computing and Decision Sciences

2019

Vol. 44, No. 2

151--178

Sentiment classification is an important task which gained extensive attention both in academia and in industry. Many issues related to this task such as handling of negation or of sarcastic utterances were analyzed and accordingly addressed in previous works. However, the issue of class imbalance which often compromises the prediction capabilities of learning algorithms was scarcely studied. In this work, we aim to bridge the gap between imbalanced learning and sentiment analysis. An experimental study including twelve imbalanced learning preprocessing methods, four feature representations, and a dozen of datasets, is carried out in order to analyze the usefulness of imbalanced learning methods for sentiment classification. Moreover, the data difficulty factors - commonly studied in imbalanced learning - are investigated on sentiment corpora to evaluate the impact of class imbalance.