Information Retrieval
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combining Classifiers through Triplet-Based Belief Functions
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
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In this paper we present an alternative evidential method of combining prioritized decisions, in order to arrive at a "consensus", or aggregate, decision. Previous studies have suggested that, in some classification domains, the better performance can be achieved through combining the first and second decisions from each evidence source. However, it is easy to illustrate the fact that going further down a decision list, to give longer preferred decisions, can provide the alternative to the method of combining only the first one and second decisions. Our objective here is to examine the theoretical aspect of an alternative method in terms of quartet茂戮驴 how extending a decision list of any length by one extra preferred decision affects classification results. We also present the experimental results to demonstrate the effectiveness of our alternative method.