An automatic library data classification system using layer structure and voting strategy

Abstract

This paper deals with issues of traditional one-layered book classification systems and employs the complementary attribute of various classifiers to propose a two layered book classification system using voting strategy. Moreover, the collection of dissertations from a university library and books from an electronic bookstore are used as the training and testing corpus. The classification codes of dissertations and books are employed as the gold standard as well. Each dissertation contains various components such as title, authors, table of contents, abstract or cited papers et al. To understand the classification effect of all the combinations of components, various combinations are studied as well and the best combination is recommended. The features extracted from abstracts and table of content are found to be most useful for document classification. On the other hand, to obtain the best classification performance, the combination of classifiers for a two-layered book classification system is studied and the best combination was also recommended as well.