Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Models for retrieval with probabilistic indexing
Information Processing and Management: an International Journal - Modeling data, information and knowledge
A probabilistic learning approach for document indexing
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic Indexing: An Experimental Inquiry
Journal of the ACM (JACM)
A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Classifying text documents by associating terms with text categories
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Two Odds-Radio-Based Text Classification Algorithms
WISEW '02 Proceedings of the Third International Conference on Web Information Systems Engineering (Workshops) - (WISEw'02)
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
An Evaluation of Approaches to Classification Rule Selection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Best terms: an efficient feature-selection algorithm for text categorization
Knowledge and Information Systems
The effect of threshold values on association rule based classification accuracy
Data & Knowledge Engineering
CCIC: Consistent Common Itemsets Classifier
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Statistical Identification of Key Phrases for Text Classification
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Document-Base Extraction for Single-Label Text Classification
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Practical application of associative classifier for document classification
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Threshold tuning for improved classification association rule mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Textual Feature Selection (TFS) is an important phase in the process of text classification. It aims to identify the most significant textual features (i.e. key words and/or phrases), in a textual dataset, that serve to distinguish between text categories. In TFS, basic techniques can be divided into two groups: linguistic vs. statistical. For the purpose of building a language-independent text classifier, the study reported here is concerned with statistical TFS only. In this paper, we propose a novel statistical TFS approach that hybridizes the ideas of two existing techniques, DIAAF (Darmstadt Indexing Approach Association Factor) and RS (Relevancy Score). With respect to associative (text) classification, the experimental results demonstrate that the proposed approach can produce greater classification accuracy than other alternative approaches.