TY - GEN
T1 - Supervised multi-class classification with adaptive and automatic parameter tuning
AU - Chao, Chen
AU - Shyu, Mei Ling
AU - Chen, Shu Ching
N1 - Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - In this paper, a classification framework is developed to address the issue that empirical determination of the parameters and their values typically makes a classification framework less adaptive and general to different data sets and application domains. Experimental results show that our proposed framework achieves (1) better performance over other comparative supervised classification methods, (2) more robust to imbalanced data sets, and (3) smaller performance variance to different data sets.
AB - In this paper, a classification framework is developed to address the issue that empirical determination of the parameters and their values typically makes a classification framework less adaptive and general to different data sets and application domains. Experimental results show that our proposed framework achieves (1) better performance over other comparative supervised classification methods, (2) more robust to imbalanced data sets, and (3) smaller performance variance to different data sets.
UR - http://www.scopus.com/inward/record.url?scp=70449363820&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449363820&partnerID=8YFLogxK
U2 - 10.1109/IRI.2009.5211595
DO - 10.1109/IRI.2009.5211595
M3 - Conference contribution
AN - SCOPUS:70449363820
SN - 9781424441167
T3 - 2009 IEEE International Conference on Information Reuse and Integration, IRI 2009
SP - 433
EP - 434
BT - 2009 IEEE International Conference on Information Reuse and Integration, IRI 2009
T2 - 2009 IEEE International Conference on Information Reuse and Integration, IRI 2009
Y2 - 10 August 2009 through 12 August 2009
ER -