1 research outputs found
Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes
Active learning aims to achieve greater accuracy with less training data by
selecting the most useful data samples from which it learns. Single-criterion
based methods (i.e., informativeness and representativeness based methods) are
simple and efficient; however, they lack adaptability to different real-world
scenarios. In this paper, we introduce a multiple-criteria based active
learning algorithm, which incorporates three complementary criteria, i.e.,
informativeness, representativeness and diversity, to make appropriate
selections in the active learning rounds under different data types. We
consider the selection process as a Determinantal Point Process, which good
balance among these criteria. We refine the query selection strategy by both
selecting the hardest unlabeled data sample and biasing towards the classifiers
that are more suitable for the current data distribution. In addition, we also
consider the dependencies and relationships between these data points in data
selection by means of centroidbased clustering approaches. Through evaluations
on synthetic and real-world datasets, we show that our method performs
significantly better and is more stable than other multiple-criteria based AL
algorithms