# Exploiting Domain Knowledge to Address Class Imbalance in Meteorological Data Mining
## 2 The Problem of Class Imbalance  
A very good introduction to the problem of class imbalance and the related research efforts is given in [4; 5]. Ref. [4] provides a comprehensive review of the subject and discusses the initial solutions that were proposed to deal with the problem of class imbalance. Ref. [5] discusses the role that rare classes and rare cases play in data mining, the problems that they can cause and the methods that have been proposed to address these problems.  
Over the years the problem of class imbalanced has been studied extensively. There exist numerous papers that use standard data agnostic oversampling and undersampling techniques to create balanced training datasets. Regarding meteorological data, ref. [6] first applies oversampling to increase thunderstorm examples in the training dataset and then uses deep neural networks to predict thunderstorms. Similarly, ref. [7] applies standard oversampling techniques on radar image data to improve rainfall prediction, while [8] presents a framework for predicting floods, in which it embeds re-sampling to address class imbalance. Finally, ref. [9] does not apply any sampling strategies but experiments with various classifiers and concludes that Self-Growing Neural Networks perform better when predicting fog events using data with class imbalance.  
Various research works attempt to exploit domain knowledge to address the class imbalance problem, but not in the meteorological domain. Ref. [10] addresses the problem of noisy and borderline examples when using oversampling methods, while [11] deals simultaneously with the problems of class imbalance and class overlap. Ref. [12] uses domain specific knowledge to address the problem of class imbalance in text sentiment classification. Finally, ref. [13] exploits domain knowledge to address multi-class imbalance in classification tasks for manufacturing data.  
In our study, we use the most common sampling strategies found in the literature to address the class imbalance problem, namely, the randomized undersampling and the SMOTE oversampling methods and their combination. SMOTE stands for Synthetic Minority Oversampling Technique [14]. Besides the natural distribution, we employ the commonly used 30% and 50% (or balanced) distributions regarding the minority class [1]. We also examine the within-class distribution in addition to the between-class distribution [15], using a combination of the randomized undersampling and the SMOTE oversampling methods in both minority and majority examples.  
In an effort to take into account the peculiarities of the data domain when sampling the training datasets and to examine how these could affect the performance of the classifiers, we applied two novel strategies when constructing balanced datasets, i.e., datasets where the number of majority and minority examples is equal. In the first strategy, we applied the k-Means clustering algorithm using \"classes to clusters\" evaluation to select only the most homogeneous majority examples. In the second strategy, we rejected the majority examples that were closer to the minority examples with respect to their temporal distance in days using three different values for the distance. Then, we further reduced the number of majority examples to achieve a balanced distribution using the randomized undersampling method. We are not aware of any other attempt that uses large meteorological databases and at the same time domain specific sampling techniques to address the class imbalance problem.  
We used five different classifiers to build models for predicting our class variable. The training/test set method was used to evaluate the models and to reveal the best sampling strategy for meteorological data. As an evaluation metric, we used the Area Under the ROC (Receiver Operating Characteristics) Curve (AUC) [5,16].