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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Open-Set Classification for Automated Genre Identification
verfasst von : Dimitrios A. Pritsos, Efstathios Stamatatos
Erschienen in: Advances in Information Retrieval
Verlag: Springer Berlin Heidelberg
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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.