# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 5 Taxonomy  
In the reviewed papers, we classified the type of approach and presented a table (Table 2) with some advantages and disadvantages. The table is divided into theoretical and real-world applications, and subdivided into the three approaches commonly used to deal with class imbalance problems [15; 31; 34]. This division was used to be concise in the common categories found in the literature and follow the same methodology that we used for the systematic review of the state of the art for class imbalance problems on fuzzy and pattern-based classification (i.e., theoretical and practical approaches). Another advantage is to quickly identify the type of approach with its benefits and drawbacks. We create a taxonomy that is a summary of the similarities and differences among the different approaches. Most of the papers are focused on the algorithm level due to the possible improvement in their results. However, those approaches are limited to the specific problem to solve. In real-world applications, most of them are related to medicine, with some exceptions in finance and urban planning. Additionally, in real-world applications, it is common to combine different approaches to deal with the problem ahead to get the most of each approach (i.e., data level and algorithm level been to most common combination). Figure 1 shows the results of the subject area of the 62 papers that cover fuzzy sets, pattern-based, and imbalance problems without real-world applications. Figure 2 shows the number of publications by year. As is expected, the three main subject areas are Computer Science, Mathematics, and Engineering. Additionally, Figure 2 shows the increase in published documents in the field, where 2018 was the year with the higher number of publications across the 15 reviewed years. Figure 1: Number of published documents by subject area. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Application** & **Approach** & **Refs.** & **Advantage** & **Disadvantage** \\ \hline \multirow{4}{*}{Theoretical} & \multirow{2}{*}{Data level} & [51,63,67,69,72] & Data is artificially manipulated to deal with the imbalance problem. & The data created can lead to a bias in the classification process. \\ \cline{2-4}  & & [20,21,42,91,100,102,107,108,115,117,121,124,129,132,135,137,141,144,148,151,154] & The results from the experiments tend to have positive results due to the fitting process of the problem. & Each solution solves the imbalance problem in their own scenario, which does not present a general solution for most cases. \\ \cline{2-4}  & \multirow{2}{*}{Algorithm level} & [22] & The used methods have been deeply studied and have better results against other learning techniques. & They need to enhance how they handle classification errors. \\ \cline{2-4}  & \multirow{2}{*}{Algorithm level} & [158] & The usage of Mahalanobis distance in combination with fuzzy kNN, improves the results of normal kNN. & The results are limited to the specific case. \\ \cline{2-4}  & \multirow{2}{*}{Data level} & [159] & The approach can be used in diseases, signal, and image classification & Optimization could be improved. \\ \cline{2-4}  & \multirow{2}{*}{Algorithm level} & [162] & The author mentioned that there are room for improvements, and they have promising results. & Accuracy and sensitivity are semehow low, 76\% and 79\% respectively. \\ \cline{2-4}  & \multirow{2}{*}{Data level} & [157] & Improved robustness and classification performance in contrast of a single classifier. & The results are not known, but they will report them in future publications. \\ \hline \multirow{2}{*}{Financial} & \multirow{2}{*}{Algorithm level} & [160] & Robust results for the P2P & The decision model could be considered too simple. \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & \multirow{2}{*}{Algorithm level} & [166] & The model can adapt to abrupt changes and has great scalability. & It is unknown how it will perform outside the pilot city. \\ \hline \hline \end{tabular}
\end{table}