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
SIGIR '00 Proceedings of the 23rd 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
A Multilingual Text Mining Approach Based on Self-Organizing Maps
Applied Intelligence
On Machine Learning Methods for Chinese Document Categorization
Applied Intelligence
Fast and accurate text classification via multiple linear discriminant projections
The VLDB Journal — The International Journal on Very Large Data Bases
Spam filters: bayes vs. chi-squared; letters vs. words
ISICT '03 Proceedings of the 1st international symposium on Information and communication technologies
A Hierarchical Neural Network Document Classifier with Linguistic Feature Selection
Applied Intelligence
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
Feature selection for text classification with Naïve Bayes
Expert Systems with Applications: An International Journal
Using the self organizing map for clustering of text documents
Expert Systems with Applications: An International Journal
Beyond TFIDF weighting for text categorization in the vector space model
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Automatically computed document dependent weighting factor facility for Naïve Bayes classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A distance sum-based hybrid method for intrusion detection
Applied Intelligence
Least squares twin parametric-margin support vector machine for classification
Applied Intelligence
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This paper proposes an automatic folder allocation system for text documents through the implementation of a hybrid classification method which combines the Bayesian (Bayes) approach and the Support Vector Machines (SVMs). Folder allocation for text documents in computer is typically executed manually by the user. Every time the user creates text documents by using text editors or downloads the documents from the internet, and wishes to store these documents on the computer, the user needs to determine and allocate the appropriate folder in which to store these new documents. This situation is inconvenient as repeating the folder allocation each time a text document is stored becomes tedious especially when the numbers and layers of folders are huge and the structure is complex and continuously growing. This problem can be overcome by implementing Artificial Intelligence machine learning methods to classify the new text documents and allocate the most appropriate folder as the storage for them. In this paper we propose the Bayes-SVMs hybrid classification framework to perform the tedious task of automatically allocating the right folder for text documents in computers.