One-class svms for document classification
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
An N-Gram Based Approach to Automatically Identifying Web Page Genre
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
Learning to recognize webpage genres
Information Processing and Management: an International Journal
Classifying factored genres with part-of-speech histograms
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Multiple sets of features for automatic genre classification of web documents
Information Processing and Management: an International Journal
A survey of recent trends in one class classification
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
Authorship attribution in the wild
Language Resources and Evaluation
Detection of text quality flaws as a one-class classification problem
Proceedings of the 20th ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
Automated Genre Identification (AGI) of web pages is a problem of increasing importance since web genre (e.g. blog, news, e-shops, etc.) information can enhance modern Information Retrieval (IR) systems. The state-of-the-art in this field considers AGI as a closed-set classification problem where a variety of web page representation and machine learning models have intensively studied. In this paper, we study AGI as an open-set classification problem which better formulates the real world conditions of exploiting AGI in practice. Focusing on the use of content information, different text representation methods (words and character n-grams) are tested. Moreover, two classification methods are examined, one-class SVM learners, used as a baseline, and an ensemble of classifiers based on random feature subspacing, originally proposed for author identification. It is demonstrated that very high precision can be achieved in open-set AGI while recall remains relatively high.