# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 3 Pattern and Fuzzy Approaches for Imbalance Problems
### Algorithm Level
They combined support vector decision boundary points from the majority class with an equal number of points from the minority class. After they obtained a balanced dataset, they used SVM [125] as the base learner for new instances, and they tested their method against C4.5 [52], Naive Bayes [126], Random forest [127], and AdaBoost [128]. The used 13 databases were from the KEEL repository [55]. In their results, they showed that their method outperforms the other classifiers. In 2019, [PERSON] et al. [129] proposed an algorithm based on adjustable fuzzy classification with a multi-objective genetic strategy based on decomposition. Their algorithm is called AFC-MOGD, which generates fuzzy rules and then those rules are optimized to get new fuzzy rules. They used an optimization algorithm based on decomposition with the purpose of designing a new pattern. They consider the class percentage to determine the class label and the rule weight to get more viable rules. They used 11 databases from the KEEL [55] repository and for the evaluation metric, they used area under the ROC convex hull (AUCH). They compared their algorithm against MOGF-CS [130], C4.5 [52], and E-Algorithm [131]. Their results showed they the new implementation outperforms other classifiers. In 2019, [PERSON] et al. [132] proposed an instance-based entropy fuzzy support vector machine (IEFSVM). The fuzzy membership of each sample is determined by the entropy information of kNN, which focuses on the importance of each sample. They used polar coordinates to determine the entropy function of each sample. They take into consideration the diversity of entropy to increase the size of the neighbors k. They used 35 databases for the UCI [54] repository and 12 real-world databases. They used AUC to evaluate their performance against SVM [16], undersampling SVM, cost-sensitive SVM, fuzzy SVM [114], entropy fuzzy SVM [102], cost-sensitive AdaBoost [133], cost-sensitive RF [93], EasyEnsemble [105], RUSBoost [85], and weighted ELM [134]. In their results, they proved that IEFSVM has a higher AUC value than the other methods and the results are less sensitive to noise. In 2019, [PERSON] et al. [135] proposed a multilabel classification for complex activity recognition. They used emerging patterns and fuzzy sets. Their classification process uses a training dataset of simple activities, where they extract strong jumping emerging patterns (SJEPs). Then, their scoring function uses SJEPs and fuzzy membership values, which result in the labels of the existing activities. They assumed that all the activities are linearly separated. They used three evaluation metrics: precision, recall, and F-measure. The results outperform the other compared crisp methods (i.e., SVM [16], k-NN [18], and HMM [136]). In 2019, [PERSON] et al. [137] proposed a combination of adaptive K-nearest neighbor (ADPTKNN) and fuzzy K-nearest neighbor. The adaptive K-nearest neighbor part is used to overcome the difficulties presented in fuzzy K-nearest neighbor when it deals with imbalanced databases. Their new addition chooses different values of k for different classes according to their sizes. They tested their work in: ten databases, different values of K (i.e., 5-25 in steps of 5), and evaluates F-measure, AUC, and G-mean. They compared their algorithm (Fuzzy ADPTKNN) with NWKNN [138], Adpt-NWKNN [139], and Fuzzy NWKNN [140]. In most of the presented cases, their proposed method performs better than the other approaches. In 2019, [PERSON] et al. [42] proposed an algorithm for big data environments (BD-EFEP). They claimed it is the first multi-objective evolutionary algorithm for pattern mining in big data. BD-EFEP has the objective of extract high-quality emerging patterns that describes the discriminative characteristics of the data. BD-EFEP follows a competitive-cooperative schema, where the patterns compete with each other, but they cooperate to describe the space's greatest possible area.