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
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Method for Controlling Errors in Two-Class Classification
COMPSAC '99 23rd International Computer Software and Applications Conference
A MINSAT Approach for Learning in Logic Domains
INFORMS Journal on Computing
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Comparison of Several Ensemble Methods for Text Categorization
SCC '04 Proceedings of the 2004 IEEE International Conference on Services Computing
On Combining Classifier Mass Functions for Text Categorization
IEEE Transactions on Knowledge and Data Engineering
A New Text Categorization Technique Using Distributional Clustering and Learning Logic
IEEE Transactions on Knowledge and Data Engineering
Feature selection strategies for text categorization
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A neuro-fuzzy immune inspired classifier for task-oriented texts
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Lsquare and k-NN classifiers are two machine learning approaches for text classification. Rocchio is the classic method for text classification in information retrieval. Our approach is a supervised method, meaning that the list of categories should be defined and a set of training data should be provided for training the system. In this approach, documents are represented as vectors where each component is associated with a particular word.We propose voting method and OWA operator and Decision Template method for combining classifiers. In these we use an effective and efficient new method called variance-mean based feature filtering method of feature selection. Best feature selection method and combination of methods are used to do feature reduction in the representation phase of text classification is proposed. Using this efficient feature selection method and best classifier combination method we improve the text classification performance.