Research Article | OPEN ACCESS
A Feature-Weighted Instance-Based Learner for Deep Web Search Interface Identification
1Hong Wang, 1Qingsong Xu, 2Youyang Chen and 2Jinsong Lan
1Department of Mathematics
2Department of Information Science and Engineering, Central South University, China
Research Journal of Applied Sciences, Engineering and Technology 2013 4:1278-1283
Received: June 28, 2012 | Accepted: August 08, 2012 | Published: February 01, 2013
Abstract
Determining whether a site has a search interface is a crucial priority for further research of deep web databases. This study first reviews the current approaches employed in search interface identification for deep web databases. Then, a novel identification scheme using hybrid features and a feature-weighted instance-based learner is put forward. Experiment results show that the proposed scheme is satisfactory in terms of classification accuracy and our feature-weighted instance-based learner gives better results than classical algorithms such as C4.5, random forest and KNN.
Keywords:
Deep web mining, instance-based learning, search interface identification,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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