Integer Programming for Multi-class Active Learning

by Dragomir Yankov, Suju Rajan and Adwait Ratnaparkhi

This paper proposed an integer programming based method for active learning in multiclass discriminative tasks. The idea is to select enough samples for each one-vs-rest SVM based on margin-distance uncertainty and we hope especially those may contributed to more classifiers are encouraged. That's for each sample we put an indicator saying whether it is selected as training. The constraints are for each classifier, the selected samples must be enough (according to the rank of uncertainty, we take a little more than required candidates so that this constraint may be satisfied). This is solved by feasibility pump (implemented by lpsolve).