CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-06-06
Cited: 0
Clicked: 6605
Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0002-9978-3337
Li Deng, Xin Du, Ji-zhong Shen. Web page classification based on heterogeneous features and a combination of multiple classifiers[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(7): 995-1004.
@article{title="Web page classification based on heterogeneous features and a combination of multiple classifiers",
author="Li Deng, Xin Du, Ji-zhong Shen",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="7",
pages="995-1004",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900240"
}
%0 Journal Article
%T Web page classification based on heterogeneous features and a combination of multiple classifiers
%A Li Deng
%A Xin Du
%A Ji-zhong Shen
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 7
%P 995-1004
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900240
TY - JOUR
T1 - Web page classification based on heterogeneous features and a combination of multiple classifiers
A1 - Li Deng
A1 - Xin Du
A1 - Ji-zhong Shen
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 7
SP - 995
EP - 1004
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900240
Abstract: Precise web page classification can be achieved by evaluating features of web pages, and the structural features of web pages are effective complements to their textual features. Various classifiers have different characteristics, and multiple classifiers can be combined to allow classifiers to complement one another. In this study, a web page classification method based on heterogeneous features and a combination of multiple classifiers is proposed. Different from computing the frequency of HTML tags, we exploit the tree-like structure of HTML tags to characterize the structural features of a web page. Heterogeneous textual features and the proposed tree-like structural features are converted into vectors and fused. Confidence is proposed here as a criterion to compare the classification results of different classifiers by calculating the classification accuracy of a set of samples. Multiple classifiers are combined based on confidence with different decision strategies, such as voting, confidence comparison, and direct output, to give the final classification results. Experimental results demonstrate that on the Amazon dataset, 7-web-genres dataset, and DMOZ dataset, the accuracies are increased to 94.2%, 95.4%, and 95.7%, respectively. The fusion of the textual features with the proposed structural features is a comprehensive approach, and the accuracy is higher than that when using only textual features. At the same time, the accuracy of the web page classification is improved by combining multiple classifiers, and is higher than those of the related web page classification algorithms.
[1]Ali F, Khan P, Riaz K, et al., 2017. A fuzzy ontology and SVM-based web content classification system. IEEE Access, 5:25781-25797.
[2]Baskin II, Marcou G, Horvath D, et al., 2017. Bagging and boosting of classification models. In: Varnek A (Ed.), Tutorials in Chemoinformatics, Wiley Online Library, p.241-247.
[3]Cai D, Yu SP, Wen JR, et al., 2003. Extracting content structure for web pages based on visual representation. Asia-Pacific Web Conf, p.406-417.
[4]Elsalmy F, Ismail R, Abdelmoez W, 2017. Enhancing web page classification models. Int Conf on Advanced Intelligent Systems and Informatics, p.742-750.
[5]Gers FA, Schmidhuber J, Cummins F, 2000. Learning to forget: continual prediction with LSTM. Neur Comput, 12(10): 2451-2471.
[6]Gogar T, Hubacek O, Sedivy J, 2016. Deep neural networks for web page information extraction. IFIP Int Conf on Artificial Intelligence Applications and Innovations, p.154-163.
[7]Heinrich G, 2017. Evaluation of a distribution-based web page classification. In: Friedrichsen M, Kamalipour Y (Eds.), Digital Transformation in Journalism and News Media. Springer, Cham, p.55-68.
[8]Kumari KP, Reddy AV, 2012. Performance improvement of web page genre classification. Int J Comput Appl, 53(10): 24-27.
[9]Li HK, Xu Z, Li T, et al., 2017. An optimized approach for massive web page classification using entity similarity based on semantic network. Fut Gener Comput Syst, 76: 510-518.
[10]Mikolov T, Chen K, Corrado G, et al., 2013. Efficient estimation of word representations in vector space. https://arxiv.org/abs/1301.3781
[11]Onan A, 2015. Artificial immune system based web page classification. In: Silhavy R, Senkerik R, Oplatkova Z, et al. (Eds.), Software Engineering in Intelligent Systems. Springer, Cham, p.189-199.
[12]Onan A, 2016. Classifier and feature set ensembles for web page classification. J Inform Sci, 42(2):150-165.
[13]Panchekha P, Torlak E, 2016. Automated reasoning for web page layout. ACM SIGPLAN Not, 51(10):181-194.
[14]Pritsos DA, Stamatatos E, 2013. Open-set classification for automated genre identification. European Conf on Information Retrieval, p.207-217.
[15]Qi XG, Davison BD, 2006. Knowing a web page by the company it keeps. Proc 15th ACM Int Conf on Information and Knowledge Management, p.228-237.
[16]Qi XG, Davison BD, 2009. Web page classification: features and algorithms. ACM Comput Surv, 41(2):12.
[17]Sze V, Chen YH, Yang TJ, et al., 2017. Efficient processing of deep neural networks: a tutorial and survey. Proc IEEE, 105(12):2295-2329.
[18]Wei YL, Wang W, Wang BL, et al., 2017. A method for topic classification of web pages using LDA-SVM model. Chinese Int Automation Conf, p.589-596.
[19]Xue WM, Bao H, Huang WM, et al., 2006. Web page classification based on SVM. 6th World Congress on Intelligent Control and Automation, p.6111-6114.
[20]Zhu J, Xie Q, Yu SI, et al., 2016. Exploiting link structure for web page genre identification. Data Min Knowl Discov, 30(3):550-575.
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