Generating Synthetic Samples to Improve Small Sample Learning with Mixed Numerical and Categorical Attributes The small data learning issue has existed for over one hundred years (since 1908) when the Studentu0027s tdistribution was first developed. Few statistical tools can evaluate a population appropriately if the sample size is too small; small samples can be remedied through virtual sample generation (VSG) methods, which are widely used in industry and machine learning. However, most VSG methods were developed for data having only numerical attributes, very few studies have dealt with nominal attributes and cause domain estimation limitations. Therefore, this paper proposes a method that generates virtual samples based on the discrete degrees of nominal attributes, and then estimates the general population domains by fuzzy membership functions. A backpropagation neural network model and a support vector regression model are used to test the efficiency of the proposed method, while the Wilcoxonsign test is used to test the difference with raw data sets. The result shows that the proposed method can reduce the mean absolute error and enhance classification accuracy by generating virtual samples that have nominal attributes.

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.