|
Published Articles >> Table of Contents >> Abstract
Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD'06)
pp. 85-90
Text Classification by Combining Different Distance Functions withWeights
Takahiro Yamada, Aichi Institute of Technology, Japan
Kyohei Yamashita, Aichi Institute of Technology, Japan
Naohiro Ishii, Aichi Institute of Technology, Japan
Kazunori Iwata, Aichi University, Japan
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SNPD-SAWN.2006.69
Send link to a friend
| Abstract |
|
Since data is becoming greatly large in the networks,
the machine classification of the text data, is not easy under
these computing circumstances. Though the k-nearest
neighbor (kNN) classification is a simple and effective classification
approach, the improving performance of the classifier
is still attractive to cope with the high accuracy processing.
In this paper, the kNN is improved by applying the
different distance functions with weights to measure data
from the multi-view points. Then, the weights for the optimization,
are computed by the genetic algorithms. After the
learning of the trained data, the unknown data is classified
by combining the multiple distance functions and ensemble
computations of the kNN. In this paper we present a new
approach to combine multiple kNN classifiers based on different
distance functions, which improve the performance
of the k-nearest neighbor method. The proposed combining
algorithm shows the higher generalization accuracy when
compared to other conventional learning algorithms.
|
 Additional Information
|
Citation:
Takahiro Yamada, Kyohei Yamashita, Naohiro Ishii, Kazunori Iwata,
"Text Classification by Combining Different Distance Functions withWeights,"
snpd-sawn,
pp. 85-90,
Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD'06),
2006
|
|
