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
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
Rough set algorithms in classification problem
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Using Correspondence Analysis to Combine Classifiers
Machine Learning
Combining Classifiers by Constructive Induction
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Feature Engineering for Text Classification
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast and accurate text classification via multiple linear discriminant projections
The VLDB Journal — The International Journal on Very Large Data Bases
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Issues in stacked generalization
Journal of Artificial Intelligence Research
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Rough Ensemble Classifier: A Comparative Study
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Hypertext Classification Using Tensor Space Model and Rough Set Based Ensemble Classifier
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Application of rough ensemble classifier to web services categorization and focused crawling
Web Intelligence and Agent Systems
Classification of web services using tensor space model and rough ensemble classifier
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Research on rough set theory and applications in China
Transactions on rough sets VIII
Two-level hierarchical combination method for text classification
Expert Systems with Applications: An International Journal
A novel split and merge technique for hypertext classification
Transactions on rough sets XII
Autonomous knowledge-oriented clustering using decision-theoretic rough set theory
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Video event retrieval from a small number of examples using rough set theory
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Rough set based ensemble classifier
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Event retrieval in video archives using rough set theory and partially supervised learning
Multimedia Tools and Applications
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Combining the results of a number of individually trained classification systems to obtain a more accurate classifier is a widely used technique in pattern recognition. In this article, we have introduced a rough set based meta classifier to classify web pages. The proposed method consists of two parts. In the first part, the output of every individual classifier is considered for constructing a decision table. In the second part, rough set attribute reduction and rule generation processes are used on the decision table to construct a meta classifier. It has been shown that (1) the performance of the meta classifier is better than the performance of every constituent classifier and, (2) the meta classifier is optimal with respect to a quality measure defined in the article. Experimental studies show that the meta classifier improves accuracy of classification uniformly over some benchmark corpora and beats other ensemble approaches in accuracy by a decisive margin, thus demonstrating the theoretical results. Apart from this, it reduces the CPU load compared to other ensemble classification techniques by removing redundant classifiers from the combination.