TY - GEN
T1 - Directly identify unexpected instances in the test set by entropy maximization
AU - Sha, Chaofeng
AU - Xu, Zhen
AU - Wang, Xiaoling
AU - Zhou, Aoying
PY - 2009
Y1 - 2009
N2 - In real applications, a few unexpected examples unavoidably exist in the process of classification, not belonging to any known class. How to classify these unexpected ones is attracting more and more attention. However, traditional classification techniques can't classify correctly unexpected instances, because the trained classifier has no knowledge about these. In this paper, we propose a novel entropy-based method to the problem. Finally, the experiments show that the proposed method outperforms previous work in the literature.
AB - In real applications, a few unexpected examples unavoidably exist in the process of classification, not belonging to any known class. How to classify these unexpected ones is attracting more and more attention. However, traditional classification techniques can't classify correctly unexpected instances, because the trained classifier has no knowledge about these. In this paper, we propose a novel entropy-based method to the problem. Finally, the experiments show that the proposed method outperforms previous work in the literature.
UR - https://www.scopus.com/pages/publications/67649977494
U2 - 10.1007/978-3-642-00672-2_67
DO - 10.1007/978-3-642-00672-2_67
M3 - 会议稿件
AN - SCOPUS:67649977494
SN - 9783642006715
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 659
EP - 664
BT - Advances in Data and Web Management - Joint International Conferences, APWeb/WAIM 2009, Proceedings
PB - Springer Verlag
T2 - Joint International Conference on Advances in Data and Web Management, APWeb/WAIM 2009
Y2 - 2 April 2009 through 4 April 2009
ER -