Weight Adjustment Technique in the Deep Web Data Source Classification Applied Research

Jia Qiang Dong

doi:10.4028/www.scientific.net/AMM.220-223.2920

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 220-223Weight Adjustment Technique in the Deep Web Data...

Weight Adjustment Technique in the Deep Web Data Source Classification Applied Research

Abstract:

The Web database's classification is the key step which integrates with the Web database classification and retrieves. The traditional search engine is unable to correct search for the magnanimous information in Deep Web hides. 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 220-223)

Pages:

2920-2923

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.220-223.2920

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Online since:

November 2012

Authors:

Jia Qiang Dong

Keywords:

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

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