Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
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Machine Learning - The Eleventh Annual Conference on computational Learning Theory
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Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A MFoM learning approach to robust multiclass multi-label text categorization
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Efficient index-based KNN join processing for high-dimensional data
Information and Software Technology
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Genetic algorithms for approximate similarity queries
Data & Knowledge Engineering
Automated Free Text Classification of Economic Activities Using VG-RAM Weightless Neural Networks
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
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MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
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ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Automatic text categorization based on content analysis with cognitive situation models
Information Sciences: an International Journal
A global-ranking local feature selection method for text categorization
Expert Systems with Applications: An International Journal
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Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In automated multi-label text categorization, an automatic categorization system should output a label set, whose size is unknown a priori, for each document under analysis. Many machine learning techniques have been used for building such automatic text categorization systems. In this paper, we examine virtual generalizing random access memory weightless neural networks (VG-RAM WNN), an effective machine learning technique which offers simple implementation and fast training and test, as a tool for building automatic multi-label text categorization systems. We evaluated the performance of VG-RAM WNN on two real-world problems:, (i) categorization of free-text descriptions of economic activities and (ii) categorization of Web pages, and compared our results with that of the multi-label lazy learning approach (Multi-Label K-Nearest Neighbors, ML-KNN). Our experimental comparative analysis showed that, on average, VG-RAM WNN either outperforms ML-KNN or show similar categorization performance.