Visual content-based web page categorization with deep transfer learning and metric learning

López-Sánchez, Daniel; Arrieta, Angélica González; Corchado, Juan M.

doi:10.1016/j.neucom.2018.08.086

Cited by 35 publications

(24 citation statements)

References 15 publications

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“…Nowadays, the trend of data mining applications in healthcare is remarkable as there are huge datasets in this sector that require a deep analysis by effective model-based techniques derived from artificial intelligence and machine learning areas. Large scale artificial intelligence and machine learning tools are increasingly successful in image-based diagnosis and have been employed for medical decision making [ 1 , 2 , 3 , 4 , 5 ] and other complex problems like scene and web pages categorization [ 6 , 7 , 8 ], retinal images classification [ 9 ] and action recognition [ 10 ]. The manual processing of these tasks is difficult, tedious and time consuming and so it is important to move to automatic methods, which are able to learn models from labeled and non-labeled data and allow faster and more accurate decisions.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition

Alharithi

Almulihi

Bourouis

et al. 2021

Sensors

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In this paper, we propose a novel hybrid discriminative learning approach based on shifted-scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address some challenging problems of medical data categorization and recognition. The main goal is to capture accurately the intrinsic nature of biomedical images by considering the desirable properties of both generative and discriminative models. To achieve this objective, we propose to derive new data-based SVM kernels generated from the developed mixture model SSDMM. The proposed approach includes the following steps: the extraction of robust local descriptors, the learning of the developed mixture model via the expectation–maximization (EM) algorithm, and finally the building of three SVM kernels for data categorization and classification. The potential of the implemented framework is illustrated through two challenging problems that concern the categorization of retinal images into normal or diabetic cases and the recognition of lung diseases in chest X-rays (CXR) images. The obtained results demonstrate the merits of our hybrid approach as compared to other methods.

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Section: Introductionmentioning

confidence: 99%

Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition

Alharithi

Almulihi

Bourouis

et al. 2021

Sensors

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“…(2018) propose an automatic categorization of web service elements by means of Logistic Model Trees-based classifier, in conjunction with a data pre-processing technique that reduces the original feature-space dimension without affecting data integrity. López-Sánchez, Arrieta & Corchado (2019) propose a framework for the categorization of web pages on the basis of their visual content by means of a Deep Convolutional Neural Network for feature extraction. The detection of specific features on webpages or websites has been studied in the following works.…”

Section: Introductionmentioning

confidence: 99%

Website categorization: A formal approach and robustness analysis in the case of e-commerce detection

Bruni

Bianchi

2020

Expert Systems with Applications

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Website categorization has recently emerged as a very important task in several contexts. A huge amount of information is freely available through websites, and it could be used for example to accomplish statistical surveys, saving in costs. However, the information of interest for the specific categorization has to be mined among that huge amount. This turns out to be a difficult task in practice. In this work we propose a practically viable procedure to perform website

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“…To this end, human editors manually classified sites-often relying on suggested categories submitted by other users-into a purpose-specific taxonomy [4]. The quick expansion of the Internet soon put this approach to an end and led to the development of automated classification solutions [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Mis-shapes, Mistakes, Misfits

Vallina

Pochat

Feal

et al. 2020

Proceedings of the ACM Internet Measurement Conference

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Domain classification services have applications in multiple areas, including cybersecurity, content blocking, and targeted advertising. Yet, these services are often a black box in terms of their methodology to classifying domains, which makes it difficult to assess their strengths, aptness for specific applications, and limitations. In this work, we perform a large-scale analysis of 13 popular domain classification services on more than 4.4M hostnames. Our study empirically explores their methodologies, scalability limitations, label constellations, and their suitability to academic research as well as other practical applications such as content filtering. We find that the coverage varies enormously across providers, ranging from over 90% to below 1%. All services deviate from their documented taxonomy, hampering sound usage for research. Further, labels are highly inconsistent across providers, who show little agreement over domains, making it difficult to compare or combine these services. We also show how the dynamics of crowd-sourced efforts may be obstructed by scalability and coverage aspects as well as subjective disagreements among human labelers. Finally, through case studies, we showcase that most services are not fit for detecting specialized content for research or content-blocking purposes. We conclude with actionable recommendations on their usage based on our empirical insights and experience. Particularly, we focus on how users should handle the significant disparities observed across services both in technical solutions and in research. CCS CONCEPTS• Networks → Network measurement; • Information systems → Clustering and classification; Web applications; Web searching and information discovery.ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Visual content-based web page categorization with deep transfer learning and metric learning

Cited by 35 publications

References 15 publications

Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition

Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition

Website categorization: A formal approach and robustness analysis in the case of e-commerce detection

Mis-shapes, Mistakes, Misfits

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