Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2001

Abstract

Hierarchical Classification refers to assigning of one or more suitable categories from a hierarchical category space to a document. While previous work in hierarchical classification focused on virtual category trees where documents are assigned only to the leaf categories, we propose atop-down level-based classification method that can classify documents to both leaf and internal categories. As the standard performance measures assume independence between categories, they have not considered the documents incorrectly classified into categories that are similar or not far from the correct ones in the category tree. We therefore propose the Category-Similarity Measures and Distance-Based Measures to consider the degree of misclassification in measuring the classification performance. An experiment has been carried out to measure the performance four proposed hierarchical classification method. The results showed that our method performs well for Reuters text collection when enough training documents are given andthe new measures have indeed considered the contributions of misclassified documents.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

IEEE International Conference on Data Mining, 29 November-2 December 2001, San Jose, California: Proceedings

First Page

521

Last Page

528

ISBN

9780769511191

Identifier

10.1109/ICDM.2001.989560

Publisher

IEEE

City or Country

San Jose, CA, USA

Additional URL

http://doi.org/10.1109/ICDM.2001.989560

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