Meta-Learning Algorithms for Few-Shot Classification in Imbalanced Datasets

This work aims to examine meta-learning on few-shot learning schemes, specifically on the assessment of how well current approaches manage imbalance in the examined datasets. Meta-learning to improve model performance with a small amount of data by learning from related tasks is tested against benchmarks such as MAML, ProtoNet and RelationNet. An important insight of our research is that these methods are still facing important issues, such as scalability or the problem of domain generalization, as well as the major issue of the extreme imbalance of the data distribution. To overcome these problems, we suggest a new solution, which employs elaborate methods and is characterized by increased correspond and F1 coefficient, as well as the degree of balanced accuracy at different degrees of unbalanced data. However, the proposed study also points out the main challenges for future research including new and efficient algorithms for high-dimensional and real-time scenario, better methods for handling massive shift, as well as the possibility to increase the interpretability of models. The research also highlights the possibilities of applying meta-learning in the few-shot classification, but at the same time also highlight the imperfections and the research needs to be continued in order to further improve the existing gaps and to meet the practical requirements. This study adds to the overall knowledge base concerning meta learning approaches and provides an initial frameworkfor work to be conducted in the future in an effort to produce methods that are considerably more portable, generalizable and also more comprehensible in nature across the broader field of study.