Paper Title:
Patent Classification Using Hybrid Classifier Systems
  Abstract

Patents are distributed through hundreds of collections, divided up by general area. A hybrid classifier system thus can be a powerful solution to difficult patent classification problems. In this study, we present a system for classifying patent documents on a hybrid approach by combining multiple text classifiers (Naïve Bayes, KNN and Rocchio). Decisions made by various text classifiers can be combined by voting and sampling mechanisms in the system. A prototype system was developed and tested in a real world task. The results have indicated that the accuracy of the hybrid approach is more stable than that of any of the three individual text classifiers.

  Info
Periodical
Edited by
Yanwen Wu
Pages
458-463
DOI
10.4028/www.scientific.net/AMR.187.458
Citation
S. H. Liu, H. L. Liao, S. M. Pi, C. C. Kao, "Patent Classification Using Hybrid Classifier Systems", Advanced Materials Research, Vol. 187, pp. 458-463, 2011
Online since
February 2011
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