Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes

TL;DR

This paper proposes a multiple-criteria active learning method using fixed-size Determinantal Point Processes to select diverse, informative, and representative samples, improving learning efficiency across various data types.

Contribution

It introduces a novel active learning algorithm combining three criteria within a DPP framework, enhancing adaptability and stability over existing methods.

Findings

Outperforms existing multiple-criteria active learning algorithms

Provides more stable and accurate sample selection

Effective on both synthetic and real-world datasets

Abstract

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.