Classification of Deep Web Data Sources Based on Feature Weight Estimate

DOI

10.2991/icaicte.2013.44How to use a DOI?

Keywords

deep web; web database; feature extraction; feature valuation; Naive Bayes classifier

Abstract

The traditional search engine is unable to correct search for the magnanimous information in Deep Web hides. The Web database's classification is the key step which integrates with the Web database classification and retrieves. This article has proposed one kind of classification based on machine learning's web database. The experiment has indicated that after this taxonomic approach undergoes few sample training, it can achieve the very good classified effect, and along with training sample's increase, this classifier's performance maintains stable and the rate of accuracy and the recalling rate fluctuate in the very small scope.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaoqing ZHOU
AU  - Jiaxiu SUN
AU  - Shubin Wang
PY  - 2013/08
DA  - 2013/08
TI  - Classification of Deep Web Data Sources Based on Feature Weight Estimate
BT  - Proceedings of the 2013 International Conference on Advanced ICT and Education
PB  - Atlantis Press
SP  - 207
EP  - 210
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaicte.2013.44
DO  - 10.2991/icaicte.2013.44
ID  - ZHOU2013/08
ER  -