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Published Articles >> Table of Contents >> Abstract
2006 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
pp. 57-62
An Effective Hybrid Classifier Based on Rough Sets and Neural Networks
Rujiang Bai, Shandong University of Technology Library, China
Xiaoyue Wang, Shandong University of Technology Library, China
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WI-IATW.2006.36
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| Abstract |
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Due to the exponential growth of documents on the
Internet and the emergent need to organize them, the
automated categorization of documents into predefined
labels has received an ever-increased attention in the
recent years. This paper describes a method developed
for the automatic clustering of documents by using a
hybrid classifier based on rough sets and neural
networks, which we called as Rough-Ann,. First, the
documents are denoted by vector space model and the
feature vectors are reduced by using rough sets. Then
using those feature vectors we reduced that are
training set for artificial neural network and clustering
the documents. The experimental results show that the
algorithm Rough-Ann is effective for the documents
classification, and has the better performance in
classification precision, stability and fault-tolerance
comparing with the traditional classification methods,
Bayesian classifiers SVM and kNN, especially for the
complex classification problems with many feature
vectors.
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 Additional Information
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Citation:
Rujiang Bai, Xiaoyue Wang,
"An Effective Hybrid Classifier Based on Rough Sets and Neural Networks,"
wi-iatw,
pp. 57-62,
2006 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops,
2006
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