Web page classification based on heterogeneous features and a combination of multiple classifiers

Deng, Li; Du, Xin; Shen, Jizhong

doi:10.1631/fitee.1900240

Cited by 10 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… seven studies that used a combination of HTML tag structure and text content, as shown in [10], [11], [19], [20], [24], [25], [27]  six studies that used images as shown in [9], [12]- [14], [16], [17]  three studies that used the feature of HTML tags structure as shown in [8], [15], [28]  two studies that used each feature of text content as shown in [22], [23]  two studies that used URL features as shown in [21],…”

Section: Resultsmentioning

confidence: 99%

Automatic Topic-Based Web Page Classification Using Deep Learning

Apandi,

Sallim,

Mohamed

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

The internet is frequently surfed by people by using smartphones, laptops, or computers in order to search information online in the web. The increase of information in the web has made the web pages grow day by day. The automatic topic-based web page classification is used to manage the excessive amount of web pages by classifying them to different categories based on the web page content. Different machine learning algorithms have been employed as web page classifiers to categorise the web pages. However, there is lack of study that review classification of web pages using deep learning. In this study, the automatic topic-based classification of web pages utilising deep learning that has been proposed by many key researchers are reviewed. The relevant research papers are selected from reputable research databases. The review process looked at the dataset, features, algorithm, pre-processing used in classification of web pages, document representation technique and performance of the web page classification model. The document representation technique used to represent the web page features is an important aspect in the classification of web pages as it affects the performance of the web page classification model. The integral web page feature is the textual content. Based on the review, it was found that the image based web page classification showed higher performance compared to the text based web page classification. Due to lack of matrix representation that can effectively handle long web page text content, a new document representation technique which is word cloud image can be used to visualize the words that have been extracted from the text content web page.

show abstract

Section: Resultsmentioning

confidence: 99%

Automatic Topic-Based Web Page Classification Using Deep Learning

Apandi,

Sallim,

Mohamed

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…The task of multi-label website classification has received little research attention; however, we focused on it as it would help us to establish our methodology. In the multilabel website classification context, Deng and Shen [49] approached the methodology by combining deep learning and machine learning to benefit from the ability to extract high-level features from a large amount of raw data and the ability to process high-dimensional features provided by LSTM and SVM, respectively. The proposed method was better than using SVM or LSTM independently in terms of accuracy.…”

Section: B: Multi-label Website Classificationmentioning

confidence: 99%

Intelligent Framework for Detecting Predatory Publishing Venues

Ateeq

Al‐Khalifa

2023

IEEE Access

View full text Add to dashboard Cite

Predatory publishing venues publish questionable articles and pose a global threat to the integrity and quality of the scientific literature. They have given rise to the dark side of scholarly publishing and their effects have reached political, societal, economic, and health aspects. Given their consequences and proliferation, several solutions have been developed to help detect them; however, these solutions are manual and time-consuming. While researchers, students, and readers are in need of a tool that automatically detects predatory venues and their violations, in this study, we proposed an intelligent framework that can automatically detect predatory venues and their violations using different artificial intelligence techniques. This work contributes through the following: (1) creating a dataset of 9,866 journals annotated as predatory and legitimate, and (2) proposing an intelligent framework for classifying a venue as legitimate or predatory, with appropriate reasoning. Our framework was evaluated using seven different machine learning and deep learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Networks (NNs), Long short-term memory (LSTM), Convolutional Neural Network (CNN), Bidirectional Encoders from Transformers (BERT), A Lite BERT (ALBERT), and different feature representation techniques. The results showed that the CNN model outperformed the other models in journal classification task, with an F1 score of 0.96. For appropriate reasoning of the provisioning task, the SVM model achieved the best micro F1 of 0.67.

show abstract

“…In a similar spirit to our work, researchers in the past also explored the effectiveness of visual features in classifying Web pages (de Boer et al 2010). More recently, re- searchers have started to explore deep architectures based on LSTM (Deng, Du, and Shen 2020), GRU (Du, Han, and Zhao 2018), and BERT (Gupta and Bhatia 2021) applied to textual and HTML features. Although, unlike us, focusing only on a single language or a subset of very popular websites, these directions have shown how performance can be improved with the help of more complex models.…”

Section: Related Workmentioning

confidence: 99%

Homepage2Vec: Language-Agnostic Website Embedding and Classification

Lugeon

Piccardi

West

2022

ICWSM

View full text Add to dashboard Cite

Currently, publicly available models for website classification do not offer an embedding method and have limited support for languages beyond English. We release a dataset of more than two million category-labeled websites in 92 languages collected from Curlie, the largest multilingual human-edited Web directory. The dataset contains 14 website categories aligned across languages. Alongside it, we introduce Homepage2Vec, a machine-learned pre-trained model for classifying and embedding websites based on their homepage in a language-agnostic way. Homepage2Vec, thanks to its feature set (textual content, metadata tags, and visual attributes) and recent progress in natural language representation, is language-independent by design and generates embedding-based representations. We show that Homepage2Vec correctly classifies websites with a macro-averaged F1-score of 0.90, with stable performance across low- as well as high-resource languages. Feature analysis shows that a small subset of efficiently computable features suffices to achieve high performance even with limited computational resources. We make publicly available the curated Curlie dataset aligned across languages, the pre-trained Homepage2Vec model, and libraries: https://github.com/epfl-dlab/homepage2vec.

show abstract

Web page classification based on heterogeneous features and a combination of multiple classifiers

Cited by 10 publications

References 19 publications

Automatic Topic-Based Web Page Classification Using Deep Learning

Automatic Topic-Based Web Page Classification Using Deep Learning

Intelligent Framework for Detecting Predatory Publishing Venues

Homepage2Vec: Language-Agnostic Website Embedding and Classification

Contact Info

Product

Resources

About