# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 3 Pattern and Fuzzy Approaches for Imbalance Problems
### Algorithm Level
To improve efficiency in evaluating the individual without losing quality, they used a MapReduce-based global approach or a token competition-based procedure. In conclusion, BD-EFEP obtains a set of patterns with an improvement in the trade-off between the generality and the reliability of the results. These patterns are extracted faster than other approaches, which makes the algorithm relevant for big data environments. In 2019, [PERSON] et al. [141] presented the effects of different quality measures that are used as objectives for multi-objectives approaches, focused on the extraction of emerging patterns (EPs) in big data environments. EPs are a type of contrast patterns extracted by using the quality measure Growth Rate, which is computed using the mean of the highest ratio between the support of a certain pattern in one class and the support of the same pattern in the other classes [41]. They used eight combinations of quality measures:Jac and TPR, G-mean and Jac, Jac and FPR, G-mean and WRacc, Jac and WRacc, SuppDiff and Jac, TPR and FPR, and WRAcc and SupDiff [142]. They performed the experiments in six large-scale databases from the UCI repository [54]. The number of instances goes up to 11 million, but the number of variables is low. They used the BD-EFEP [42] algorithm, which is one of the two algorithms for the extraction of EPs (i.e., EvAEFP-Spark [143] and the BD-EFEP). In their results, they showed that the combination of TPR and FPR, G-mean and Jac, and Jac and FPR, are the most suitable quality measures for the descriptive rules in emerging pattern mining. Nevertheless, Jac and FPR is shown as the best trade-off in the descriptive rules for EPs. In 2019, [PERSON] et al. [144] proposed three different subgroup discovery approaches for mining subgroups in multiple instances problems. The proposed approaches were based on an exhaustive search approach (SD-Map [145]), an evolutionary algorithm based on grammar-guided genetic programming (CGBA-SD [146]), and an evolutionary fuzzy system (NMEEF-SD [147]). Those approaches are adapted to multiple instance problems. The approaches were tested in two different steps: first, they used ten benchmarks representing real-world applications, and then, 20 artificial databases generated by themselves. Their results showed that SD-Map-MI has poor performance with low quality and bad trade-off between sensitivity and confidence. CGBA-SD-MI has a good trade-off between sensitivity and confidence, as well as good performance in the different databases. NMEEF-SD-MI also has rules of low quality and bad results on confidence and low sensitivity; however, some databases present a confidence level higher than 83%. Finally, they conclude that they need to keep performing more tests because it can be a promising approach to medicine or Bioinformatics problems. In 2020, [PERSON] et al. [148] proposed a method based on an adaptive version of the NSGA-II algorithm. They proposed a cooperative-competitive multi-objective evolutionary fuzzy system called E2 PAMEA. The algorithm is used to extract emerging patterns in big data environments. E2 PAMEA uses an adaptive schema that selects different genetic operators. The genetic operators used for diversity are two-point and HUX crossover. Similar to their previous work [42], they used a cooperative-competitive schema along with a token competition-based procedure for reliable results. Finally, E2 PAMEA outperforms previous approaches showing that their results are more reliable, and the processing time and physical memory are also improved. In 2020, [PERSON] [21] presented an extension of the fuzzy support vector machines for class imbalance learning (FSVM-CIL) method. The idea is to overcome the limitation of FSVM-CIL for imbalanced databases with borderline noise (i.e., between-class borderline). To solve this limitation, a new distance measure and a new fuzzy function are introduced.