Application of Weight Adjustment Technique in the Deep Web Data Source Classification

Xiao Qing Zhou; Jia Xiu Sun; Shu Bin Wang

doi:10.4028/www.scientific.net/AMM.397-400.2367

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 397-400Application of Weight Adjustment Technique in the...

Application of Weight Adjustment Technique in the Deep Web Data Source Classification

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.

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Periodical:

Applied Mechanics and Materials (Volumes 397-400)

Pages:

2367-2370

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.397-400.2367

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Online since:

September 2013

Authors:

Xiao Qing Zhou, Jia Xiu Sun, Shu Bin Wang

Keywords:

Deep Web, Feature Extraction, Feature Valuation, Naive Bayes Classifier, Web Database

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References

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