Combining Classifiers to Extract Web Data

DOI

10.2991/iccse-15.2015.76How to use a DOI?

Keywords

Web Data Extraction; Ensemble Learning; Data Integration

Abstract

A lot of data on the web are usually embedded in the semi-structured pages. In order to automatically process the content embedded in Web pages, extracting data from them and making it available to computer applications remains a complex and urgent task. Most of current approaches use a single classifier to extract web data, but relying on a single classifier is not sufficient and different classifier has different performance for a problem. In this paper, we combine multiple classifiers to extract web data. Firstly, we identify the main data regions of web pages, and construct feature sets of text nodes in the regions. Secondly, we choose three kinds of base classifiers and then use the voting method to integrate results of each classifier. Finally, we combine integration results with heuristic rules to get the final extraction results. The experiment results show that our approach outperforms the baseline approaches.

Copyright

© 2015, 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  - Qiang Chu
AU  - Yongquan Dong
AU  - Ping Ling
PY  - 2015/07
DA  - 2015/07
TI  - Combining Classifiers to Extract Web Data
BT  - Proceedings of the 2015 International Conference on Computational Science and Engineering
PB  - Atlantis Press
SP  - 412
EP  - 416
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccse-15.2015.76
DO  - 10.2991/iccse-15.2015.76
ID  - Chu2015/07
ER  -