A Study on the Impact of Data Characteristics in Imbalanced Regression Tasks 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 enduser preferences are biased towards values of the target variable that are underrepresented on the available data. Several preprocessing 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 preprocessing 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 preprocessing methods for handling imbalanced regression tasks.

The Impact of Data Preparation on the Fairness of Software Systems Machine learning models are widely adopted in scenarios that directly affect people. The development of software systems based on these models raises societal and legal concerns, as their decisions may lead to the unfair treatment of individuals based on attributes like race or gender. Data preparation is key in any machine learning pipeline, but its effect on fairness is yet to be studied in detail. In this paper, we evaluate how the fairness and effectiveness of the learned models are affected by the removal of the sensitive attribute, the encoding of the categorical attributes, and instance selection methods (including crossvalidators and random undersampling). We used the Adult Income and the German Credit Data datasets, which are widely studied and known to have fairness concerns. We applied each data preparation technique individually to analyse the difference in predictive performance and fairness, using statistical parity difference, disparate impact, and the normalised prejudice index. The results show that fairness is affected by transformations made to the training data, particularly in imbalanced datasets. Removing the sensitive attribute is insufficient to eliminate all the unfairness in the predictions, as expected, but it is key to achieve fairer models. Additionally, the standard random undersampling with respect to the true labels is sometimes more prejudicial than performing no random undersampling.