TY - GEN
T1 - A transductive extension of maximum entropy/iterative scaling for decision aggregation in distributed classification
AU - Miller, David Jonathan
AU - Zhang, Yanxin
AU - Kesidis, George
PY - 2008
Y1 - 2008
N2 - Many ensemble classification systems apply supervised learning to design a function for combining classifier decisions, which requires common labeled training samples across the classifier ensemble. Without such data, fixed rules (voting, Bayes rule) are usually applied. [1] alternatively proposed a transductive constraint-based learning strategy to learn how to fuse decisions even without labeled examples. There, decisions on test samples were chosen to satisfy constraints measured by each local classifier. There are two main limitations of that work. First, feasibility of the constraints was not guaranteed. Second, heuristic learning was applied. Here we overcome both problems via a transductive extension of maximum entropy/improved iterative scaling for aggregation in distributed classification. This method is shown to achieve improved decision accuracy over the earlier transductive approach on a number of UC Irvine data sets.
AB - Many ensemble classification systems apply supervised learning to design a function for combining classifier decisions, which requires common labeled training samples across the classifier ensemble. Without such data, fixed rules (voting, Bayes rule) are usually applied. [1] alternatively proposed a transductive constraint-based learning strategy to learn how to fuse decisions even without labeled examples. There, decisions on test samples were chosen to satisfy constraints measured by each local classifier. There are two main limitations of that work. First, feasibility of the constraints was not guaranteed. Second, heuristic learning was applied. Here we overcome both problems via a transductive extension of maximum entropy/improved iterative scaling for aggregation in distributed classification. This method is shown to achieve improved decision accuracy over the earlier transductive approach on a number of UC Irvine data sets.
UR - http://www.scopus.com/inward/record.url?scp=51449097925&partnerID=8YFLogxK
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U2 - 10.1109/ICASSP.2008.4517997
DO - 10.1109/ICASSP.2008.4517997
M3 - Conference contribution
AN - SCOPUS:51449097925
SN - 1424414849
SN - 9781424414840
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1865
EP - 1868
BT - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
T2 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Y2 - 31 March 2008 through 4 April 2008
ER -