Context-sensitive learning methods for text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical neural networks for text categorization (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Text classification using ESC-based stochastic decision lists
Proceedings of the eighth international conference on Information and knowledge management
A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature selection for text categorization on imbalanced data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Best terms: an efficient feature-selection algorithm for text categorization
Knowledge and Information Systems
Expert Systems with Applications: An International Journal
Geometric particle swarm optimization
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Document-Base Extraction for Single-Label Text Classification
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Text classification based on multi-word with support vector machine
Knowledge-Based Systems
A two-stage feature selection method for text categorization
Computers & Mathematics with Applications
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Rapid growth of digital information requires automated handling and organization of documents. The two main stages in automated document categorization are (i) term reduction and (ii) classification. In this paper, we present a novel two-stage term reduction strategy based on Information Gain (IG) theory and Geometric Particle Swarm Optimization (GPSO) search. We evaluate performance of the proposed term reduction approach with use of a new classifier, fuzzy unordered rule induction algorithm (FURIA) to categorize multi-label texts. In order to evaluate the performance of FURIA quantitatively, we compared it against two widely used algorithms, Naive Bayes and Support Vector Machine (SVM). Text Categorization (TC) performance of the proposed term reduction strategy is validated with use of Reuters-21578 and OHSUMED text collection datasets. The experimental results show that performance of the proposed term reduction method is efficient for document organization tasks.