Evaluating text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
Variable precision rough set model
Journal of Computer and System Sciences
The nature of statistical learning theory
The nature of statistical learning theory
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in 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
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
The Combination of Text Classifiers Using Reliability Indicators
Information Retrieval
A novel refinement approach for text categorization
Proceedings of the 14th ACM international conference on Information and knowledge management
A model for handling approximate, noisy or incomplete labeling in text classification
ICML '05 Proceedings of the 22nd international conference on Machine learning
Two-Level Hierarchical Hybrid SVM-RVM Classification Model
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Rough set Based Ensemble Classifier forWeb Page Classification
Fundamenta Informaticae
Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification
Information Sciences: an International Journal
Precision and Recall in Rough Support Vector Machines
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
A rough margin based support vector machine
Information Sciences: an International Journal
Rough set based hybrid algorithm for text classification
Expert Systems with Applications: An International Journal
Improved Classification for Problem Involving Overlapping Patterns
IEICE - Transactions on Information and Systems
Rough Cluster Quality Index Based on Decision Theory
IEEE Transactions on Knowledge and Data Engineering
An effective refinement strategy for KNN text classifier
Expert Systems with Applications: An International Journal
SV-kNNC: an algorithm for improving the efficiency of k-nearest neighbor
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
An examination of feature selection frameworks in text categorization
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Computational Biology and Chemistry
Class-indexing-based term weighting for automatic text classification
Information Sciences: an International Journal
Automated crime report analysis and classification for e-government and decision support
Proceedings of the 14th Annual International Conference on Digital Government Research
| Hi-index | 12.05 |
Text classification has been recognized as one of the key techniques in organizing digital data. The intuition that each algorithm has its bias data and build a high performance classifier via some combination of different algorithm is a long motivation. In this paper, we proposed a two-level hierarchical algorithm that systematically combines the strength of support vector machine (SVM) and k nearest neighbor (KNN) techniques based on variable precision rough sets (VPRS) to improve the precision of text classification. First, an extension of regular SVM named variable precision rough SVM (VPRSVM), which partitions the feature space into three kinds of approximation regions, is presented. Second, a modified KNN algorithm named restrictive k nearest neighbor (RKNN) is put forward to reclassify texts in boundary region effectively and efficiently. The proposed algorithm overcomes the drawbacks of sensitive to noises of SVM and low efficiency of KNN. Experimental results compared with traditional algorithms indicate that the proposed method can improve the overall performance significantly.