A novel active learning framework for classification: Using weighted rank aggregation to achieve multiple query criteria

Highlights：

• It is the first work to realize the MQCAL method for classification task by introducing weighted rank aggregation methods.

• A mechanism is presented that allows a self-adaptive tradeoff between any number and kind of sample query criteria.

• The proposed method is of high scalability and generality, and no longer needs any empirical parameters for weights.

• The potentially best combination of sample query criteria and rank aggregation approaches is given through experiments.

• Comparing with other AL methods, our proposed methods (RMQCAL) can achieve higher accuracy with less labeling costs.