Active Learning With Optimal Instance Subset Selection.

Yifan Fu, xingquan zhu, Ahmed K. Elmagarmid

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Active learning (AL) traditionally relies on some instance-based utility measures (such as uncertainty) to assessindividual instances and label the ones with the maximum values for training. In this paper, we argue that such approaches cannotproduce good labeling subsets mainly because instances are evaluated independently without considering their interactions, and in-dividuals with maximal ability do not necessarily form an optimal instance subset for learning. Alternatively, we propose to achieveAL with optimal subset selection (ALOSS), where the key is to find an instance subset with a maximum utility value. To achievethe goal, ALOSS simultaneously considers the following: 1) the importance of individual instances and 2) the disparity betweeninstances, to build an instance-correlation matrix. As a result, AL is transformed to a semidefinite programming problem to selecta k -instance subset with a maximum utility value. Experimental results demonstrate that ALOSS outperforms state-of-the-artapproaches for AL.
Original languageEnglish
Pages (from-to)464-475
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume43
Issue number2
Publication statusPublished - Apr 2013

Fingerprint Dive into the research topics of 'Active Learning With Optimal Instance Subset Selection.'. Together they form a unique fingerprint.

  • Cite this