# An Empirical Assessment of Performance of Data Balancing Techniques in Classification Task
## 1 Introduction  
Classification is a supervised machine learning (ML) technique used to predict class label of unseen data by building a classifier using historical data. Classification algorithms usually work with the assumption that dataset used to build the classifier is balanced. However, many datasets are highly imbalanced. An imbalanced dataset refers to a dataset where one class outnumbers the other classes in the dataset with respect to the target class variable. For example, consider a dataset that contains 1000 transactions, out of which 990 are nonfraudulent and only 10 are fraudulent transactions. This is a good example of a highly imbalanced dataset. Many such examples of imbalanced datasets in classification tasks are discussed in the literature. Some of them are software product defect detection [1], survival prediction of hepatocellular carcinoma patients [2], customer churn prediction [3], predicting freshmen student attrition [4], insurance fraud detection [5], and intrusion and crime detection [6]. When we build a classifier using a highly imbalanced dataset, the classifier is usually biased towards the majority class cases. It means that the performance of the classifier will be better at correctly predicting majority class cases than minority class cases. However, in real life we expect that a classifier should be unbiased and equally good at correctly predicting both minority and majority cases. Therefore, balancing imbalanceddatasets is one of the most important activities because it helps to reduce bias in the model prediction, and thereby enhances the classifier's performance. To address the problem of imbalanced datasets in the classification task, several solutions have been proposed in the literature [7; 8; 9]. These solutions are broadly divided into several categories, namely data-preprocessing-level solutions, cost-sensitive learning methods, algorithm-level solutions, and ensemble methods. The data-preprocessing-level solutions are based on resampling of the original data. Resampling is performed before building the classifier. Therefore, resampling techniques are easy to implement and are independent of the classifier. Cost-sensitive learning approaches take into account the significance of misclassification of majority and minority class instances. Algorithm-level solutions either suggest a new algorithm or modify existing algorithms. Algorithm-level solutions are dependent on algorithms and require a detailed understanding of the algorithm for implementation. Therefore, algorithm-level solutions are less popular compared to resampling techniques. Ensemble solutions combine ensemble (bagging and boosting) models with resampling techniques or a cost-sensitive approach [7; 8]. Though several solutions have been proposed in the literature to deal with the imbalanced dataset problem in classification tasks, there is a lack of research assessing the performance of DBT [7]. As a large number of solutions have been proposed in the literature, it is difficult to assess the performance of all proposed DBT. Therefore, in this study we limited the scope of our study to assess the performance of only resampling techniques used to balance the imbalanced dataset at the data-preprocessing level. The reason for choosing resampling techniques was that these techniques are very widely used in the literature to deal with imbalanced dataset problems in classification tasks. The objectives of this study are: (1) to assess performance of DBT used to balance the imbalanced dataset; (2) to assess whether performance of DBT is independent of the level of imbalance ratio in the dataset; (3) to assess whether performance of DBT is independent of the classifier; (4) to assess whether DBT help to improve the performance of the classifiers. The rest of the paper is organized as follows. Section 2 describes the theoretical background and related work. In Section 3, we discuss the experimental setup. The results of the experiment are analyzed and discussed in Section 4. Finally, the paper is concluded in Section 5.