Chinese text classification by the Naïve Bayes Classifier and the associative classifier with multiple confidence threshold values

Authors:
Shing-Hwa Lu;Ding-An Chiang;Huan-Chao Keh;Hui-Hua Huang
Affiliations:
Department of Urology, School of Medicine, National Yang-Ming University, Taiwan and Department of Urology, Taipei City Hospital, Taipei, Taiwan;Department of Computer Science and Information Engineering, Tamkang University, Taipei, Taiwan;Department of Computer Science and Information Engineering, Tamkang University, Taipei, Taiwan;Department of Computer Science and Information Engineering, Tamkang University, Taipei, Taiwan
Venue:
Knowledge-Based Systems
Year:
2010

Citing 12
Cited 4

Term-weighting approaches in automatic text retrieval

Information Processing and Management: an International Journal
Growing decision trees on support-less association rules

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining confident rules without support requirement

Proceedings of the tenth international conference on Information and knowledge management
A Lazy Approach to Pruning Classification Rules

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Introducing a Family of Linear Measures for Feature Selection in Text Categorization

IEEE Transactions on Knowledge and Data Engineering
Improving classification performance using unlabeled data: Naive Bayesian case

Knowledge-Based Systems
A review of associative classification mining

The Knowledge Engineering Review
Text Categorization Based on Boosting Association Rules

ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
Alternative prior assumptions for improving the performance of naïve Bayesian classifiers

Data Mining and Knowledge Discovery
Structure identification of Bayesian classifiers based on GMDH

Knowledge-Based Systems
CBAR: an efficient method for mining association rules

Knowledge-Based Systems
Techniques for improving the performance of naive bayes for text classification

CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing

NB+: An improved Naïve Bayesian algorithm

Knowledge-Based Systems
Intelligent Naïve Bayes-based approaches for Web proxy caching

Knowledge-Based Systems
Improving the performance of association classifiers by rule prioritization

Knowledge-Based Systems
Improving classification accuracy of associative classifiers by using k-conflict-rule preservation

Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication

Quantified Score

Hi-index	0.00

Visualization

Abstract

Each type of classifier has its own advantages as well as certain shortcomings. In this paper, we take the advantages of the associative classifier and the Naive Bayes Classifier to make up the shortcomings of each other, thus improving the accuracy of text classification. We will classify the training cases with the Naive Bayes Classifier and set different confidence threshold values for different class association rules (CARs) to different classes by the obtained classification accuracy rate of the Naive Bayes Classifier to the classes. Since the accuracy rates of all selected CARs of the class are higher than that obtained by the Naive Bayes Classifier, we could further optimize the classification result through these selected CARs. Moreover, for those unclassified cases, we will classify them with the Naive Bayes Classifier. The experimental results show that combining the advantages of these two different classifiers better classification result can be obtained than with a single classifier.