Paper Info
Title
Hierarchical Document Classification Based on a Backtracking Algorithm
Abstract
Hierarchical document classification refers to assigning one or more suitable categories from a hierarchical category space to a document. This paper proposes a new hierarchical document classification method based on a backtracking algorithm. Utilizing the relationships between categories in category tree, a suitable threshold for every category is found to determine whether a document could be classified into the category. And the backtracking algorithm in our hierarchical classification approach effectively solves the problem that a misclassification at higher level directly leads to the misclassification at a lower level. Moreover, feature set is selected by integrating information gain with hierarchy information, which accords with the characteristic of a category tree. Experiments show that the method performs well when enough training documents are given.
Year
DOI
Venue
2008
10.1109/FSKD.2008.346
FSKD (2)
Keywords
Field
DocType
backtracking,suitable category,backtracking algorithm,hierarchical category space,tree searching,een category,hierarchical document classification,enough training document,hierarchy information,information gain,hierarchical classification approach,feature extraction,feature set selection,category tree,hierarchical document classification method,classification,new hierarchical document classification,document handling,support vector machines,pediatrics,classification algorithms
Data mining,Computer science,Feature set,Artificial intelligence,Hierarchy,Backtracking,Document classification,Pattern recognition,Support vector machine,Information gain,Feature extraction,Statistical classification,Machine learning
Conference
Volume
ISBN
Citations 
2
978-0-7695-3305-6
1
PageRank 
References 
Authors
0.36
7
5
Name
Order
Citations
PageRank
Cuiling Zhu151.45
Jun Ma21280127.50
Dongmei Zhang31439132.94
Xiaohui Han4175.41
Xiaofei Niu5154.37