# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 3 Pattern and Fuzzy Approaches for Imbalance Problems
### Algorithm Level
In 2017, [PERSON] et al. [108] proposed an algorithm using an entropy-based fuzzy membership evaluation that enhances the importance of patterns. Their algorithm can handle the imbalance problem as well as matrix patterns. The algorithm is an entropy-based matrix pattern-oriented Ho-Kashyap learning machine with regularization learning (EMatMHKS), and it is based in the MatMHKS [109] algorithm. They divide the classification process into two steps: first, they determine the fuzzy membership for each pattern and then they used the obtained values to solve the criterion function. They used SMOTE [35], ADASYN [110], CBSO [111], EE [105], and BC [112] to preprocess the data (separately), and later they used each of the classifiers. For their experiments, they used ten real-world databases. They used accuracy, sensitivity, specificity, precision, F-measure, and G-mean as their evaluation metrics. They compared their algorithm against SVM, MatMHKS, and the three fuzzy methods FSVM, B-FSVM [113], and FSVM-CIL [114]. In the showed results, EMatMHKS has better overall performance in terms of the evaluation metrics, and it also has lower computational complexity than FSVM and B-FSVM. In 2017, [PERSON] et al. [115] proposed an approach to deal with imbalanced data by combining complementary fuzzy support vector machine (CMTFSVM) and synthetic minority oversampling technique (SMOTE). To enhance the classification performance, they used a membership function. The experiments were performed in four benchmark databases from KEEL [55] and UCI repositories [54], and one real-world data database. Their results are compared against crisp NN [116], SVM [16], and the fuzzy method, FSVM [114], showing that combining CMT undersampling technique and an oversampling technique presents the best results. In 2018, [PERSON] et al. [117] proposed a classifier that handles class imbalance and overlapping problems by geometrically separating the data. Their solution is based on fuzzy support vector machine and k-NN. First, they assign an overlap-sensitive weight scheme that used k-NN and different error cost algorithms. Then, they used an overlap-sensitive margin classifier (OSM) that separates the data into soft and hard overlap regions. For the soft region, the data is classified with the decision boundary of the OSM. For the hard region, they classified the data with 1-NN. Finally, it classified the data using the decision boundary of the separated regions. Their method was tested in 12 synthetics databases and 29 real-world databases from the KEEL [55] repository, where they were evaluated with geometric mean and F1 as their evaluation metrics. Their algorithm was compared against normal SVM, under + SVM, SMOTE + SVM, SDC [118], 1-NN [119], and Boosting SVM [120]. They also compared their algorithm against FSVM-CIL cen [114], FSVM-CIL hyp [114], EFSVM [102], and EMatMHKS [108], in which all of them are fuzzy solutions. All the results were favorable for their proposed method. In 2019, [PERSON] et al. [121] proposed two variants of entropy based fuzzy SVM (EFSVM) [102], one that uses least squares, called EFLSSVM-CIL, and another that uses twin support called entropy based fuzzy least squares twin support vector machine (EFLSTMSVM-CIL). In both cases, the fuzzy membership is calculated with the entropy values of each sample. Their algorithms use linear equations to find the decision surface, in contrast with other similar methods that use quadratic programming problem. The performance of the algorithms is evaluated with AUC and the databases used are from the KEEL [55] and UCI [54] repository. The results were tested in linear and non-linear cases, where they compared their methods against TWSVM [122], FTWSVM [122], EFSVM, and NFTWSVM [123], where the last three are fuzzy solutions. Their results showed that EFLSTWSVM-CIL outperforms the other classifiers in terms of learning speed. In 2019, [PERSON] et al. [124] proposed an under-sampling method with support vectors.