Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Noise reduction in a statistical approach to text categorization
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Making large-scale support vector machine learning practical
Advances in kernel methods
Hierarchical neural networks for text categorization (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Information Retrieval
Exploiting Hierarchy in Text Categorization
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
CONSTRUE/TIS: A System for Content-Based Indexing of a Database of News Stories
IAAI '90 Proceedings of the The Second Conference on Innovative Applications of Artificial Intelligence
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Web page classification: Features and algorithms
ACM Computing Surveys (CSUR)
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
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Categorisation of digital documents is useful for organisation and retrieval. While document categories can be a set of unstructured category labels, some document categories are hierarchically structured. This paper investigates automatic hierarchical categorisation and, specifically, the role of features in the development of more effective categorisers. We show that a good hierarchical machine learning-based categoriser can be developed using small numbers of features from pre-categorised training documents. Overall, we show that by using a few terms, categorisation accuracy can be improved substantially: unstructured leaf level categorisation can be improved by up to 8.6%, while top-down hierarchical categorisation accuracy can be improved by up to 12%. In addition, unlike other feature selection models --- which typically require different feature selection parameters for categories at different hierarchical levels --- our technique works equally well for all categories in a hierarchical structure. We conclude that, in general, more accurate hierarchical categorisation is possible by using our simple feature selection technique.