Beyond Banditron: A Conservative and Efficient Reduction for Online Multiclass Prediction with Bandit Setting Model

In this paper, we consider a recently proposed supervised learning problem, called online multiclass prediction with bandit setting model. Aiming at learning from partial feedback of online classification results, i.e. ¿true¿ when the predicting label is right or ¿false¿ when the predicting label is wrong, this new kind of problems arouses much of researchers' interest due to its close relations to real world internet applications and human cognitive procedure. While some algorithms have been brought forward, we propose a novel algorithm to deal with such problems. First, we reduce the multiclass prediction problem to binary based on Conservative one-versus-all others Reduction scheme; Then Online Passive-Aggressive Algorithm is embedded as binary learning algorithm to solve the reduced problem. Also we derive a pleasing cumulative mistake bound for our algorithm and a time complexity bound linear to the sample size. Further experimental evaluation on several real world multiclass datasets including RCV1, MNIST, 20 Newsgroups and USPS shows that our method outperforms the existing algorithms with a great improvement.