**6. Conclusion**

In this chapter, we have presented the classification problem in multi-label datasets, which is an important problem because these datasets appear in several domains. We have presented the description measures and the suitable metrics to evaluate the performances of the extracted knowledge. Then, we have reviewed the different approaches and methods used to deal, which are divided into two main categories: multi-label transformation methods and algorithm adaptation methods.

In future work, we are planning to present a state of the art about different approaches and techniques used to handle the classification problem in imbalanced multi-label datasets.
