Lexical analysis and stoplists
Information retrieval
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
ACM Transactions on Asian Language Information Processing (TALIP)
Using some web content mining techniques for Arabic text classification
DNCOCO'09 Proceedings of the 8th WSEAS international conference on Data networks, communications, computers
Batch Mode Active Learning for Networked Data
ACM Transactions on Intelligent Systems and Technology (TIST)
Evolutionary ANNs for improving accuracy and efficiency in document classification methods
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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We investigate four different classification methods for document classification. Naive Bayes classifier, nearest neighbor classifier, decision tree classifier and subspace method were applied to seven-class Yahoo newsgroups individually and in combination. We studied three classifier combination approaches: simple voting, dynamic classifier selection, and adaptive classifier combination. Our experimental results indicate that naive Bayes classifier and the subspace method outperform the other two classification methods on our data sets. Combinations of multiple classifiers did not always improve classification accuracy. Among the three different combination approaches, the adaptive classifier combination method proposed here performed the best.