# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 4 Applications Domains  
In this next section, we present the papers that the research was used directly into an application domain. As we see, most research was conducted in the field of medicine, followed by that of the financial market. In 2017, [PERSON] [157] showed strategies to handle the class imbalance problem in digital pathology. In the paper, it is showed how fuzzy rule-based algorithms can use cost-sensitive functions to include misclassification cost and how to use ensemble classification methods for imbalanced data. They evaluate the methods in the AIDPATH project [157] and are waiting to get results to make them public. In 2018, [PERSON] et al. [158] proposed a classifier named Mahalanobis fuzzy k-nearest centroid neighbor (MFkNCN). First, they calculate the k-nearest centroid neighborhood for each class. Then, they used a fuzzy membership to designate the correct class label of the query sample. They used a real-world database from the Shiraz University, where it has the adult heart sound of 112 subjects (79 healthy people and 33 patients). They used accuracy as their performance metric. They compared their classifier against kNN, kNCN, and two fuzzy approaches, FkNN and MFkCNN. Their results showed that MFkNCN outperforms the other classifiers. In 2018, [PERSON] [159] proposed a data preprocessing method to classify Parkinson, hepatitis, Pima Indians, single proton emission computed thoracic heart, and thoracic surgery medical databases. Their method is divided into three main steps. First, they used k-means, fuzzy c-means, and mean shift to calculate the cluster centers. Secondly, they calculated the absolute differences between each attribute and the cluster centers; the average of the differences of each attribute is also calculated. In the last step, they proposed three attribute weighting methods to reduce the variance within the class. If the similarityrate is low, the coefficient will be be high, and if the similarity rate is high, the coefficient will be low. Their method is compared against random sampling and they used Linear discriminant analysis, k-NN, SVM [16], and RT as classification methods. The databases are from the UCI [54] repository. The performance is evaluated with accuracy, precision. Recall, AUC, j-value, and F-measure. Finally, their results showed that their approach has better results than random sampling method. They claimed that the three proposed attribute weighting methods could be used in signal and image classification. In 2019, [PERSON] et al. [160] proposed an investment decision model for a peer-to-peer (P2P) lending market based on the instance-based entropy fuzzy support vector machine (IEFSVM). The idea of this algorithm is based on the necessity of an effective prediction method that supports decisions in the P2P lending market. First, for the training set, they select the desired features and train the loan status with IEFSVM. Secondly, for the test set, they remove the unselected features and predict the loan status using the same IEFSVM. In the end, the method selects the loans classified as fully paid. The database used is from the Lending Club, which is specialized in P2P loans. They used AUC, precision, predicted negative condition rate as their evaluation metrics. They compared their method against Cost-sensitive adaptive boosting [133], cost-sensitive random forest [93], EasyEnsemble [105], random undersampling boosting [85], weighted extreme learning machine [134], cost-sensitive extreme gradient boosting [161], and one fuzzy approach, EFSVM [102]. Their results showed that their method outperforms the other classifiers for loan status classification. In 2019, [PERSON] et al. [162] proposed a Bayesian Possibilistic C-means (BPCM) clustering algorithm that provides a preliminary cervical cancer diagnostic. They explained that the motivation of the research is due the high lethality and morbidity of the disease. The information used in the diagnostic process could be highly private and many patients decided not to provide this information.