Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Towards language independent automated learning of text categorization models
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Making large-scale support vector machine learning practical
Advances in kernel methods
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
Effective Methods for Improving Naive Bayes Text Classifiers
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Enhanced word clustering for hierarchical text classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Text categorization using weight adjusted k-nearest neighbor classification (information retrieval)
Text categorization using weight adjusted k-nearest neighbor classification (information retrieval)
Clustering documents into a web directory for bootstrapping a supervised classification
Data & Knowledge Engineering - Special issue: WIDM 2003
A New Text Categorization Technique Using Distributional Clustering and Learning Logic
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Using the self organizing map for clustering of text documents
Expert Systems with Applications: An International Journal
Text categorization with class-based and corpus-based keyword selection
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Expert Systems with Applications: An International Journal
Conceptual modeling of cardinality constraints in social publishing
International Journal of Intelligent Systems
Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
The Naive Bayes classification approach has been widely implemented in real-world applications due to its simplicity and low cost training and classifying algorithm. As a trade-off to its simplicity, the Naive Bayes technique has thus been reported to be one of the poorest-performing classification methods around. We have explored and investigated the Naive Bayes classification approach and found that one of the reasons that causes the low classification accuracy is the mis-classification of documents into several ''popular'' categories due to the improper organization of the training dataset where the distribution of training documents among categories is highly skewed. In this work, we propose a solution to the problem addressed above, which is the addition of the Automatically Computed Document Dependent (ACDD) weighting factor facility to the Naive Bayes classifier. The ACDD weighting factors are computed for the purpose of enhancing the classification performance by adjusting the probability values based on the density of classified documents in each available category to minimize the mis-classification rate.