IEEE Transactions on Pattern Analysis and Machine Intelligence
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)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Combining Multiple Learning Strategies for Effective Cross Validation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
The combination of multiple classifiers using an evidential reasoning approach
Artificial Intelligence
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
Classification by cluster analysis: a new meta-learning based approach
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
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Combining multiple classifiers via combining schemes or meta-learners has led to substantial improvements in many classification problems. One of the challenging tasks is to choose appropriate combining schemes and classifiers involved in an ensemble of classifiers. In this paper we propose a novel evidential approach to combining decisions given by multiple classifiers. We develop a novel evidence structure - a focal triplet, examine its theoretical properties and establish computational formulations for representing classifier outputs as pieces of evidence to be combined. The evaluations on the effectiveness of the established formalism have been carried out over the data sets of 20- newsgroup and Reuters-21578, demonstrating the advantage of this novel approach in combining classifiers.