Classification of Deep Web Data Sources Based on Feature Weight Estimate
Xiaoqing ZHOU, Jiaxiu SUN, Shubin Wang
Available Online August 2013.
- https://doi.org/10.2991/icaicte.2013.44How to use a DOI?
- deep web; web database; feature extraction; feature valuation; Naive Bayes classifier
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 2013 International Conference on Advanced ICT and Education (ICAICTE-13) PB - Atlantis Press SP - 207 EP - 210 SN - 1951-6851 UR - https://doi.org/10.2991/icaicte.2013.44 DO - https://doi.org/10.2991/icaicte.2013.44 ID - ZHOU2013/08 ER -