# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 3 Pattern and Fuzzy Approaches for Imbalance Problems
### Data- Level Approaches  
In this subsection, we present the most recent data-level approaches presented for imbalance problems that use patterns or fuzzy approaches. In 2017, [PERSON] et al. [51] proposed a method to handle class imbalance and missing values called fuzzy-based information decomposition (FID). FID created artificial data of the minority class. Their scheme is divided into two parts, weighting, and recovery. The weighting process is made by the fuzzy membership functions. The functions are used to measure how much the data contributes to the missing estimation. For the recovery step, they consider the contribution of the data to determine the missing values. They used a C4.5 [52] classifier with an entropy-based splitting criterion. They used 27 databases combined from different repositories (e.g., PROMISE [53], UCI [54], KEEL [55]). The evaluation metrics used for their experiments are G-mean, AUC, and Matthews correlation coefficients (MCC) [56]. Their results outperform the following crisp methods: Mix [57], KNNI [58], SOM [59], SM [35], CBOS [60], CBUS [61], ROS [62], RUS [62], and MWM [60]. In 2018, [PERSON] et al. [63] proposed a fuzzy rule-based oversampling technique (FRO) to solve the class imbalance problem. FRO uses the training data to create fuzzy rules and determine the weight of each rule. The weight of the rule represents how much the sample belongs to a fuzzy space. After the rules are created, they synthesize the artificial data according to those rules. Another contribution is that FRO can handle missing values with the best matched fuzzy rule, considering the correlation and difference between attributes. They used 55 real-world databases from the UCI [54] and KEEL [55] repository. They used AUC, recall, and F1-measure as their evaluation metrics. Their solution is compared against SMOTE [35], MWMOTE [64], RWO [65], RACOG [66], and the fuzzy method FID [51]. Their results showed that their technique is better or equal to the other compared methods and is robust in cases with overlapping. In 2020, [PERSON] [67] proposed the construction of the support-domain decision boundary implementing the fuzzy templates method. Their goal was to avoid artificial data created with oversampling techniques. Their approach includes two solutions, one based on fuzzy templates, and the second one includes the standard deviation. They compare the results obtained by RDB, ROS-RDB, FTDB [68], and SDB. In RDB and ROS-RDB, the decision boundary is defined by a line equation. For FDTB, the decision boundary includes the mean support of each class. Finally, for SDB, they included the standard deviation. The databases used for the experiments are from the KEEL [55] repository and have an imbalance degree greater than 1:9 ratio with up to 20 features. They used F1-score, precision, recall, accuracy, and geometric mean to evaluate the performance of their solution. Their results claimed to improve the process of classification against oversampling methods by avoiding an overweight in the minority class. In 2020, [PERSON] et al. [69] proposed a fuzzy representativeness difference-based oversampling technique called FRDOAC. Their method uses affinity propagation and chromosome theory of inheritance. First, they use an oversampling method, fuzzy representativeness difference (FRD), which finds a representative sample of each class according to the importance of the samples. Then, they used Mahalanobis distance [70] to calculate the representativeness of every sample. In the last step, they created artificial data based on the chromosome theory of inheritance. The comparison was made against other crisp oversampling methods such as: SMOTE [35], MWMOTE [64], RWO [65], RAGOC, and GIR [71]. They used 16 databases from the UCI [54] and KEEL [55] repository. For the evaluation metrics, they used F-measure, G-mean, and AUC. Finally, the results showed better performance than the other methods. In 2020, [PERSON] et al.