tailieunhanh - Báo cáo hóa học: " Research Article Decision Aggregation in Distributed Classification by a Transductive Extension of Maximum Entropy/Improved Iterative Scaling"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Decision Aggregation in Distributed Classification by a Transductive Extension of Maximum Entropy/Improved Iterative Scaling | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 674974 21 pages doi 2008 674974 Research Article Decision Aggregation in Distributed Classification by a Transductive Extension of Maximum Entropy Improved Iterative Scaling David J. Miller Yanxin Zhang and George Kesidis Department of Electrical Engineering The Pennsylvania State University University Park PA 16802 USA Correspondence should be addressed to David J. Miller djmiller@ Received 28 September 2007 Revised 28 January 2008 Accepted 4 March 2008 Recommended by Sergios Theodoridis In many ensemble classification paradigms the function which combines local base classifier decisions is learned in a supervised fashion. Such methods require common labeled training examples across the classifier ensemble. However in some scenarios where an ensemble solution is necessitated common labeled data may not exist i legacy proprietary classifiers and ii spatially distributed and or multiple modality sensors. In such cases it is standard to apply fixed untrained decision aggregation such as voting averaging or naive Bayes rules. In recent work an alternative transductive learning strategy was proposed. There decisions on test samples were chosen aiming to satisfy constraints measured by each local classifier. This approach was shown to reliably correct for class prior mismatch and to robustly account for classifier dependencies. Significant gains in accuracy over fixed aggregation rules were demonstrated. There are two main limitations of that work. First feasibility of the constraints was not guaranteed. Second heuristic learning was applied. Here we overcome these 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 and fixed rules on a number of UC Irvine datasets. Copyright