Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification

Understand

We address the problem of multi-class classification in the case where the number of classes is very large.

• We propose a double sampling strategy on top of a multi-class to binary reduction strategy, which transforms the original multi-class problem into a binary classification problem over pairs of examples.
• The aim of the sampling strategy is to overcome the curse of long-tailed class distributions exhibited in majority of large-scale multi-class classification problems and to reduce the number of pairs of examples in the expanded data.
• We show that this strategy does not alter the consistency of the empirical risk minimization principle defined over the double sample reduction.