Hi, I'm Josh. I'm a PhD student at the University of Liverpool and I've extended the gene reactivation function to a multi-class case. Real world data is invariably unbalanced with one class often outweighing the other, often there is a huge level of imbalance. Training on this data can lead to overfitting on the dominant class, with the smaller class being overlooked. Most methods to overcome this problem are a resumple of data which can lead to overfitting or target the loss function. Last year we proposed a method which targets the activation function. Rather than accounting for the long-tailed distribution from the output using the loss function, we accounted for the distribution in the actual model by replacing the activation function with a more suitable distribution. We used the generalized extreme value distribution. The gene reactivation is widely used in finance and meteorology to model extreme events such as market crashes and extreme weather. We proposed using the gene reactivation function as a directory placement for the sigmide activation and showed much improved results. The previous work showed that when data is balanced there is no significant difference in results between sigmide and gene-e-v. But for unbalanced data the gene-e-v gave much improved results. However this method was only available in binary classification problems. Many classification problems with unbalanced data are multi-class, so we aimed to extend the previous gene reactivation to multi-class case. We began by fitting the gene reactivation to each class separately. Then a probability is produced for each. However this is not the true probability as they do not sum to one. Similar to softmax we rescale the values so that they do sum to one, creating a true probability. We found the mg-e-v could be prone to the vanishing gradient problem similar to softmax. So we added L1 and L2 in regularization and clicked input values to deal with this. We have made code available to implement the activation at the link shown. This produces a method which is very easy to implement, requiring just a simple replacement of the final activation layer with our proposed layer. As our method changes the activation function only, other methods such as resumpling and focal loss could be used in conjunction with our method. This may produce even better results. To test our method we revisited the data used in the original gene reactivation paper. In the binary classification we originally used healthy and pneumonia versus COVID-19. Here we are able to further split the classification into a multi-class problem. Although COVID-19 is certainly not the recognition, data is often difficult to find, and the number of available cases compared to pneumonia and healthy is relatively small. In this data set we only had 129 total COVID-19 cases, and we wanted to reserve 100 to test the methods sufficiently. This gave us 20 images in the final chain set compared to 2718 images from pneumonia. Some very brief results show us as expected, for the softmax activation the recalfinumurium was high, while the other classes were lower. Our method showed some much improved and more balanced results. This suggests that the MGEV is better suited to unbalanced data than the previously used softmax activation. Here we have extended the gene reactivation function to the multi-class case. The MGEV is simple to implement, just replacing the softmax activation. On a set of highly-embalanced data we found improved results of the softmax activation. The methods can either be combined with loss-function methods. This is only preliminary work. The point of unbalanced at which the MGEV provides best performance is not yet fully understood. And the effect of the proposed activation function on other data and other metrics such as calibration has also not yet been assessed. Our rights find my supervisors and my funders.