A Study on the Impact of Data Characteristics in Imbalanced Regression Tasks

Branco, Paula, Torgo, Luís


The class imbalance problem has been thoroughly studied over the past two decades. More recently, the research community realized that the problem of imbalanced distributions also occurred in other tasks beyond classification. Regression problems are among these newly studied tasks where the problem of imbalanced domains also poses important challenges. Imbalanced regression problems occur in a diversity of real world domains such as meteorological (predicting weather extreme values), financial (extreme stock returns forecasting) or medical (anticipate rare values). In imbalanced regression the end-user preferences are biased towards values of the target variable that are under-represented on the available data. Several pre-processing methods were proposed to address this problem. These methods change the training set to force the learner to focus on the rare cases. However, as far as we know, the relationship between the data intrinsic characteristics and the performance achieved by these methods has not yet been studied for imbalanced regression tasks. In this paper we describe a study of the impact certain data characteristics may have in the results of applying pre-processing methods to imbalanced regression problems. To achieve this goal, we define potentially interesting data characteristics of regression problems. We then conduct our study using a synthetic data repository build for this purpose. We show that all the different characteristics studied have a different behaviour that is related with the level at which the data characteristic is present and the learning algorithm used. The main contributions of our work are: i) to define interesting data characteristics for regression tasks; ii) to create the first repository of imbalanced regression tasks containing 6000 data sets with controlled data characteristics; and iii) to provide insights on the impact of intrinsic data characteristics in the results of pre-processing methods for handling imbalanced regression tasks.

2017 First international workshop on learning with imbalanced domains: theory and applications, Page Range: 36-50
Paula Branco
Paula Branco
Assistant Professor

I’m an Assistant Professor at EECS, University of Ottawa. My research interests include Artificial Intelligence, Machine Learning, Imbalanced Domains, Outlier Detection, Anomaly Detection, Fraud Detection and Cybersecurity.