Machine Learning
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Using Correspondence Analysis to Combine Classifiers
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combining Multiple Learning Strategies for Effective Cross Validation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper, we describe an approach to modeling the general process of combining decisions involved in ensembles of classifiers as an evidential reasoning process. This work proposes a novel structure, theoretical properties and manipulation mechanisms for representing classifier decisions as pieces of evidence. The advantage of the representation formalism is that it not only facilitates the distinguishing of trivial focal elements from important ones, resulting in the improvement of the ensemble performance, but it also effectively reduces the computation-time from exponential (as required in the conventional process of combining multiple pieces of evidence) to linear. We have conducted a comparative analysis on the effectiveness of the proposed evidence representation formalism in the text categorization domain. By comparing this method with majority voting and the previous results, we also demonstrate the advantage of this novel approach in combining classifiers.