# Exploiting Domain Knowledge to Address Class Imbalance in Meteorological Data Mining
## 1 Introduction  
Imbalanced or skewed training datasets make predictive modeling challenging since most of the classifiers are designed assuming a uniform distribution of class labels among the examples. There are classification problems that must deal with various degrees of imbalance. The goal is to improve the quality of the training dataset, i.e., make it more balanced, in order for the classifiers to achieve better predictive performance, specifically for the minority class. Usually, the minority class is more important and, hence, the classifier should be more sensitive to classification errors for the minority class than the majority class [1]. A typical approach in the literature is the application of techniques for transforming the training dataset to balance the class distribution including data oversampling for the minority examples, data undersampling for the majority examples and combinations of these techniques [1, 2].  
We attempt to enhance existing pre-processing sampling strategies by exploiting expert knowledge from the domain of Meteorology. We use the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 40-years dataset (See [[https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-40-years](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-40-years)]([https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-40-years](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-40-years)), accessed on 29 November 2022, for details) (also known as ERA-40) and a dataset with the historical observations of the meteorological station of Mica, Thessaloniki, Greece and we attempt to predict the occurrence of precipitation on the ground at the meteorological station. We use various data pre-processing strategies (based on oversampling and undersampling) for the selection of the appropriate training dataset, and, we test their effectiveness on various classifiers.  
The input dataset consists of imbalanced data regarding the precipitation class variable, where the minority class is only the 16.1% of the cases. It is known that such situations degrade the performance of data mining or machine learning classifiers. In [3], we determined the minimum training dataset size that can ensure effective application of data mining techniques specifically on meteorological data. The performance of various classifiers did not increase significantly for training dataset sizes of more than 9 years. Also, the results were not affected by the way we chose the training dataset examples, i.e., randomly isolated examples totalling nine years versus nine entire yearly sets of examples randomly selected. In this paper, we take advantage of the above finding by choosing appropriately large training datasets for the tested classifiers.  
The contribution of this study is the proposal of effective sampling strategies on meteorological training datasets that are based on our expertise on the data domain. In our experimental study, we compare common sampling strategies from the literature and the proposed new strategies and show that the newly proposed strategies improve the performance of most classifiers.  
The remainder of the paper is organized as follows. Section 2 discusses the problem of class imbalance, reviews recent works that address it using domain knowledge, and, describes the sampling strategies used in the literature as well as the novel sampling strategies we propose. Section 3 describes the datasets we used for applying the sampling strategies on the training dataset and the classifiers that we compared. Section 4 discusses the methodology used in the experiments. In Section 5, we present the analysis and the results, and, finally, we conclude in Section 6.