摘要： A vector space model (VSM) composed of selected important features is a common way to represent documents, including patent documents. Patent documents have some special characteristics that make it difficult to apply traditional feature selection methods directly: (a) it is difficult to find common terms for patent documents in different categories; and (b) the class label of a patent document is hierarchical rather than flat. Hence, in this article we propose a new approach that includes a hierarchical feature selection (HFS) algorithm which can be used to select more representative features with greater discriminative ability to present a set of patent documents with hierarchical class labels. The performance of the proposed method is evaluated through application to two documents sets with 2400 and 9600 patent documents, where we extract candidate terms from their titles and abstracts. The experimental results reveal that a VSM whose features are selected by a proportional selection process gives better coverage, while a VSM whose features are selected with a weighted-summed selection process gives higher accuracy. 出版者： London, England: SAGE Publications 出版日期： 2012-06-01 出處： Journal of information science, 2012-06, Vol.38 (3), p.222-233 版權： The Author(s) 2012 版權： 2015 INIST-CNRS 版權： Copyright Bowker-Saur Ltd. Jun 2012 識別號： ISSN: 0165-5515 識別號： EISSN: 1741-6485 識別號： DOI: 10.1177/0165551512437635 識別號： CODEN: JISCDI
