# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 3 Pattern and Fuzzy Approaches for Imbalance Problems
### Algorithm Level
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Year** & **Ref.** & **Key Merit(s)** & **Disadvantage(s)/Improvements** \\ \hline
2011 & [20] & -Proposed a fuzzy emerging pattern technique. -Proposed the FEPC classifier. & -Fuzzy emerging patterns are highly dependent on the quality measure Growth Rate. \\ \hline
2014 & [91] & -Proposed K-Contractive Map (K-CM). & -Similar performance with a classical k-NN classifier. \\ \hline
2016 & [100] & -A study in evolutionary fuzzy systems (EFSs) for imbalance problems. & -A taxonomy of the reviewed methods is missing. \\ \hline
2016 & [102] & -Presented EFSVM. A fuzzy membership evaluation that assigns the membership value according to the class certainty. & -Unfortunately, they do not mention possible improvements in their work. \\ \hline
2017 & [107] & -Proposed KRNN based on a KNN classifier. & -KRNN could be extended to multiple classes. \\ \hline
2017 & [108] & -Proposed EMatMHKS. An algorithm with an entropy-based fuzzy membership and based in MatMHKS. & -An improvement in their function to measure the entropy-based fuzzy membership. \\ \hline
2017 & [115] & -Proposed a combination of CMTFSVM and SMOTE. entropy-based fuzzy. & -A comparison with other popular classifiers can enrich the results. \\ \hline
2018 & [117] & -Proposed a classifier based on OSM, and inspired on FSVM and k-NN. It geometrically separates the data to solve the imbalance problem. & -It uses a 1-NN algorithm for the hard-overlapping regions, which can result in a high generalization error. \\ \hline
2019 & [121] & -Proposed two variants of EFSVM. One uses least squares and the other one uses twin SVM. & -The results could improve with the implementation of heuristics solutions in the method for parameter selection. \\ \hline
2019 & [124] & -Proposed a method to generate balanced data with support vectors. & -The usage of real-world data can enrich the results of their method. \\ \hline
2019 & [129] & -Proposed AFC-MOGD. An algorithm based on adjustable fuzzy classification with a multi-objective genetic strategy. & -The usage of real-world data can enrich the results of their method. \\ \hline
2019 & [132] & -Proposed an instance-based EFSVM. & -The selection of different neighborhood sizes could improve the results due to the better knowledge of the distribution of the data. \\ \hline
2019 & [135] & -Proposed a multilabel classification for complex activity recognition. & -A comparison against other fuzzy classifiers would enrich the results. \\ \hline
2019 & [137] & -Proposed Fuzzy ADPTKNN. It combines ADPTKNN and fuzzy k-NN. & -The method could be extended to feature-based NN. \\ \hline
2019 & [42] & -Proposed BD-EFEP. An algorithm for big data environments. & -Additional comparisons against big data environments can enrich the results. \\ \hline
2019 & [141] & -Presents the effects of different quality measures in patterns, focused on Big Data Environments. & -New approaches for efficient extraction of patterns in big data environments are needed. \\ \hline \end{tabular}
\end{table} 
Table 1: Algorithm-level approaches with their key merit(s) and disadvantage(s).