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Building Hierarchical Classifiers Using Class Proximity.

Ke Wang, Senqiang Zhou, Shiang Chen Liew: Building Hierarchical Classifiers Using Class Proximity. VLDB 1999: 363-374
@inproceedings{DBLP:conf/vldb/WangZL99,
  author    = {Ke Wang and
               Senqiang Zhou and
               Shiang Chen Liew},
  editor    = {Malcolm P. Atkinson and
               Maria E. Orlowska and
               Patrick Valduriez and
               Stanley B. Zdonik and
               Michael L. Brodie},
  title     = {Building Hierarchical Classifiers Using Class Proximity},
  booktitle = {VLDB'99, Proceedings of 25th International Conference on Very
               Large Data Bases, September 7-10, 1999, Edinburgh, Scotland,
               UK},
  publisher = {Morgan Kaufmann},
  year      = {1999},
  isbn      = {1-55860-615-7},
  pages     = {363-374},
  ee        = {db/conf/vldb/WangZL99.html},
  crossref  = {DBLP:conf/vldb/99},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

In this paper, we address the need to automatically classify text documents into topic hierarchies like those in ACM Digital Library and Yahoo!. The existing local approach constructs a classifier at each split of the topic hierarchy. However, the local approach does not address the closeness of classification in hierarchical classification where the concern often is how close a classification is, rather than simply correct or wrong. Also, the local approach puts its bet on classification at higher levels where the classification structure often diminishes. To address these issues, we propose the notion of class proximity and cast the hierarchical classification as a at classification with the class proximity modeling the closeness of classes. Our approach is global in that it constructs a single classifier based on the global information about all classes and class proximity. We leverage generalized association rules as the rule/feature space to address several other issues in hierarchical classification.

Copyright © 1999 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

Online Paper

DVD Version: Load ACM SIGMOD Anthology DVD 1" and ... BibTeX

Printed Edition

Malcolm P. Atkinson, Maria E. Orlowska, Patrick Valduriez, Stanley B. Zdonik, Michael L. Brodie (Eds.): VLDB'99, Proceedings of 25th International Conference on Very Large Data Bases, September 7-10, 1999, Edinburgh, Scotland, UK. Morgan Kaufmann 1999, ISBN 1-55860-615-7
Contents BibTeX

References

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Referenced by

  1. Ke Wang, Yu He, Jiawei Han: Mining Frequent Itemsets Using Support Constraints. VLDB 2000: 43-52
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