Extreme classification is a growing research area dealing with multi-class tasks with an extremely large number of classes.
One new challenge arising in this setting is to perform inference in a reasonable time, namely quicker than common-practice multi-class algorithms which require linear time in the number of classes.
Many recent studies in this area learn hierarchical classification models, which allow logarithmic inference time.
In the project we propose a different method for inference. We show that using other prediction algorithms can improve the classification accuracy, without compromising the complexity of running time.