TY - JOUR
T1 - Active learning with optimal instance subset selection
AU - Fu, Yifan
AU - Zhu, Xingquan
AU - Elmagarmid, Ahmed K.
PY - 2013/4
Y1 - 2013/4
N2 - Active learning (AL) traditionally relies on some instance-based utility measures (such as uncertainty) to assess individual instances and label the ones with the maximum values for training. In this paper, we argue that such approaches cannot produce good labeling subsets mainly because instances are evaluated independently without considering their interactions, and individuals with maximal ability do not necessarily form an optimal instance subset for learning. Alternatively, we propose to achieve AL with optimal subset selection (ALOSS), where the key is to find an instance subset with a maximum utility value. To achieve the goal, ALOSS simultaneously considers the following: 1) the importance of individual instances and 2) the disparity between instances, to build an instance-correlation matrix. As a result, AL is transformed to a semidefinite programming problem to select a k-instance subset with a maximum utility value. Experimental results demonstrate that ALOSS outperforms state-of-the-art approaches for AL.
AB - Active learning (AL) traditionally relies on some instance-based utility measures (such as uncertainty) to assess individual instances and label the ones with the maximum values for training. In this paper, we argue that such approaches cannot produce good labeling subsets mainly because instances are evaluated independently without considering their interactions, and individuals with maximal ability do not necessarily form an optimal instance subset for learning. Alternatively, we propose to achieve AL with optimal subset selection (ALOSS), where the key is to find an instance subset with a maximum utility value. To achieve the goal, ALOSS simultaneously considers the following: 1) the importance of individual instances and 2) the disparity between instances, to build an instance-correlation matrix. As a result, AL is transformed to a semidefinite programming problem to select a k-instance subset with a maximum utility value. Experimental results demonstrate that ALOSS outperforms state-of-the-art approaches for AL.
KW - Active learning
KW - Instance subset selection
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=84890425291&partnerID=8YFLogxK
U2 - 10.1109/TSMCB.2012.2209177
DO - 10.1109/TSMCB.2012.2209177
M3 - Article
AN - SCOPUS:84890425291
SN - 2168-2267
VL - 43
SP - 464
EP - 475
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 2
ER -