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

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)32_Refereed conference paper (no ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Publication statusPublished - Jul 2021

Workshop

TitleSubset Selection in Machine Learning: From Theory to Applications (SubSetML @ ICML2021)
LocationVirtual
Period24 July 2021

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.

Research Area(s)

  • active learning, determinantial point process, k-center, batch mode

Citation Format(s)

Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes. / Zhan, Xueying; Li, Qing; Chan, Antoni B.

2021. Paper presented at Subset Selection in Machine Learning: From Theory to Applications (SubSetML @ ICML2021).

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)32_Refereed conference paper (no ISBN/ISSN)peer-review