Ranked Recall: Efficient Classification by Efficient Learning of Indices That Rank

Efficient learning and categorization in the face of myriad categories and instances is an important challenge. We investigate algorithms that efficiently learn sparse but accurate category indices. An index is a weighted bipartite graphs mapping features to categories. Given an instance, the index retrieves, scores, and ranks a set of candidate categories. The ranking or the scores can then be used for category assignment. We compare index learning against other classification approaches, including one-versus-rest and top-down classification using support vector machines. We find that the indexing approach is highly advantageous in terms of space and time efficiency, at both training and classification times, while retaining competitive accuracy. On problems with hundreds of thousands of instances and thousands of categories, the index is learned in minutes, while other methods can take orders of magnitude longer. Part of this research was performed while the author was at Yahoo! Research.