A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Sensor and Data Fusion Concepts and Applications
Sensor and Data Fusion Concepts and Applications
Information Retrieval
Machine Learning
Evidence Theory and Its Applications
Evidence Theory and Its Applications
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
The Combination of Text Classifiers Using Reliability Indicators
Information Retrieval
Computational methods for a mathematical theory of evidence
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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
Pairwise optimized Rocchio algorithm for text categorization
Pattern Recognition Letters
The impact of diversity on the accuracy of evidential classifier ensembles
International Journal of Approximate Reasoning
Text categorization methods for automatic estimation of verbal intelligence
Expert Systems with Applications: An International Journal
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In this paper we investigate the combination of four machine learning methods for text categorization using Dempster's rule of combination. These methods include Support Vector Machine (SVM), kNN (Nearest Neighbor), kNN model-based approach (kNNM), and Rocchio. We first present a general representation of the outputs of different classifiers, in particular, modeling it as a piece of evidence by using a novel evidence structure called focal element triplet. Furthermore, we investigate an effective method for combining pieces of evidence derived from classifiers generated by a 10-fold cross-validation. Finally, we evaluate our methods on the 20-newsgroup and Reuters-21578 benchmark data sets and perform the comparative analysis with majority voting in combining multiple classifiers along with the previous result. Our experimental results show that the best combined classifier can improve the performance of the individual classifiers and Dempster's rule of combination outperforms majority voting in combining multiple classifiers.