# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 7 Conclusions  
In this paper, we review and analyze the current state-of-the-art of classifiers that involved fuzzy techniques and pattern-based classifiers for solving class imbalance problems. This research was motivated by the renewed interest of the machine learning community in creating explainable artificial intelligence models and the wide interest in applying this type of model to the real world.  
Therefore, in this paper, we performed an in-depth review of current state-of-the-art techniques. We reviewed 84 papers that include fuzzy and/or pattern-based approaches for imbalanced databases, as well as papers that have cited the work of [PERSON] et al. We also included the most common applications domains. Next, we present a taxonomy that includes the type of approach with its advantages and disadvantages. Then, we include future directions of the field according to the reviewed papers. As a final part of the review, we provide the conclusions and some advantages and disadvantages of all the reviewed approaches.  
* Fuzzy and pattern-based approaches attract interest from the research community.
* Fuzzy logic is widely used for its flexibility and understandability of the results.
* Medicine is an area where the imbalance problem is constantly presented and uses the newest techniques.
* Techniques that include fuzzy approaches have shown better classification results in comparison to other classifiers based on non-fuzzy approaches.
* Fuzzy pattern-based approaches are a promising solution to handle the imbalanced data problem. However, this type of classifier should be studied further.  
* Despite the flexibility of fuzzy approaches, they can lead to repetitive solutions that are small variations of other ones.
* The quality of fuzzy patterns is highly dependent on the quality of the features of the fuzzification process.
* Fuzzy emerging patterns are highly dependent on the quality measure Growth Rate, which could not provide good patterns as stated in [142].
* The combination of fuzzy and pattern-based approaches has not been studied in detail, so some research can lead to a dead end.  
In summary, we present a survey that covers an in-depth review of papers from 2005 to 2021 that include fuzzy sets, pattern-based, imbalance problems, and documents that have cited [20]. Then, we present our analysis and show possible future directions. Finally, we finish with a conclusion of the solutions to classification for imbalanced data.  
Conceptualization, O.L.-G.; Methodology, I.L. and O.L.-G.; Software, I.L. and O.L.-G.; Validation, O.L.-G., R.M. and M.A.M.-P.; Formal analysis, I.L.; Investigation, I.L.; Resources, O.L.-G. and M.A.M.-P.; Data Curation, O.L.-G. and I.L.; Writing--Original Draft Preparation, I.L.; Writing--Review and Editing, O.L.-G., R.M. and M.A.M.-P.; Visualization, O.L.-G.; Supervision, O.L.-G., R.M. and M.A.M.-P.; Project Administration, I.L. and O.L.-G.; Funding Acquisition, R.M. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the National Council of Science and Technology of Mexico under the scholarship grant 1008244.  
Not applicable.  
The authors declare no conflict of interest.