A hybrid approach for extracting informative content from web pages

Uzun, Erdinç; Agun, Hayri Volkan; Yerlikaya, Tarık

doi:10.1016/j.ipm.2013.02.005

Cited by 47 publications

(34 citation statements)

References 23 publications

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“…A hybrid model was developed for content extraction. The informative content was extracted using certain rules . High accuracy rates for extracting informative contents were reported.…”

Section: Literature Reviewmentioning

confidence: 99%

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A hybrid FAM‐CART model for online data classification

Seera

Lim

Tan

2018

Computational Intelligence

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In this paper, an online soft computing model based on an integration between the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) for undertaking data classification problems is presented. Online FAM network is useful for conducting incremental learning with data samples, whereas the CART model prevails in depicting the knowledge learned explicitly in a tree structure. Capitalizing on their respective advantages, the hybrid FAM‐CART model is capable of learning incrementally while explaining its predictions with knowledge elicited from data samples. To evaluate the usefulness of FAM‐CART, 2 sets of benchmark experiments with a total of 12 problems are used in both offline and online learning modes. The results are examined and compared with those published in the literature. The experimental outcome positively indicates that the online FAM‐CART model is useful for tackling data classification tasks. In addition, a decision tree is produced to allow users in understanding the predictions, which is an important property of the hybrid FAM‐CART model in supporting decision‐making tasks.

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“…A hybrid model was developed for content extraction. The informative content was extracted using certain rules . High accuracy rates for extracting informative contents were reported.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The informative content was extracted using certain rules. 34 High accuracy rates for extracting informative contents were reported. For binary classification tasks, a hybrid support vector machine (SVM)-based DT was proposed.…”

Section: Hybrid Rule Extraction Modelsmentioning

confidence: 99%

A hybrid FAM‐CART model for online data classification

Seera

Lim

Tan

2018

Computational Intelligence

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“…2(a) . Whereas C j is the label of the class such that * + with and , which indicates the membership of W j in the phish class or in the legitimate class based on its corresponding features [38,39] as portrayed in Fig. 2(b).…”

Section: A Test-bed and Featuresmentioning

confidence: 99%

The Effect of Feature Selection on Phish Website Detection

Zuhair¹,

Selmat²,

Salleh³

2015

ijacsa

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Abstract-Recently, limited anti-phishing campaigns have given phishers more possibilities to bypass through their advanced deceptions. Moreover, failure to devise appropriate classification techniques to effectively identify these deceptions has degraded the detection of phishing websites. Consequently, exploiting as new; few; predictive; and effective features as possible has emerged as a key challenge to keep the detection resilient. Thus, some prior works had been carried out to investigate and apply certain selected methods to develop their own classification techniques. However, no study had generally agreed on which feature selection method that could be employed as the best assistant to enhance the classification performance. Hence, this study empirically examined these methods and their effects on classification performance. Furthermore, it recommends some promoting criteria to assess their outcomes and offers contribution on the problem at hand. Hybrid features, low and high dimensional datasets, different feature selection methods, and classification models were examined in this study. As a result, the findings displayed notably improved detection precision with low latency, as well as noteworthy gains in robustness and prediction susceptibilities. Although selecting an ideal feature subset was a challenging task, the findings retrieved from this study had provided the most advantageous feature subset as possible for robust selection and effective classification in the phishing detection domain.

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“…Los sistemas de adaptación procesan el contenido original aplicando métodos inteligentes, obteniendo una nueva versión del contenido capaz de mejorar la experiencia del usuario. Los enfoques utilizados pueden ser muy diversos: técnicas como el "aprendizaje automático" (machine learning) se utilizan para extraer las partes más relevantes del contenido [20]. Se utilizan agentes inteligentes, que eliminan las partes irrelevantes del contenido y el ruido, para de esta forma remarcar los contenidos más importantes [27].…”

Section: Trabajo Relaccionadounclassified

An intelligent Mobile Web Browser to adapt the mobile web as a function of the physical environment

Espada

García‐Díaz

Crespo

et al. 2015

IEEE Latin Am. Trans.

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Today, millions of users all over the world visit websites using their Smartphones. Mobile devices have several features that may contribute to worsen the user experience when using mobile webs, such as: small displays and virtual keyboards, poor precision to select elements using the finger as cursor. In addition, the mobile Web has a peculiar feature, is not always used in the same place or in the same situations; in many cases environmental factors can adversely affect the user experience while he is using a web application, adverse situations derived from the ambient may be different: noise pollution, an excess of light, not being able to see well the letters while walking, etc. This research presents a novel mobile Web paradigm that allows the adaptation of web applications depending on the environment. The proposed solution introduces a novel Mobile Web Browser based on the principles of the Ambient Intelligence (AmI). ThisWeb browser uses the Smartphone's hardware elements as sensors and microphones to capture the environmental information, which will be intelligently analyzed to detect events and other situations.The identified situations will be communicated to the web application, so that appropriate adaptation are made and there by improve the user experience.

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A hybrid approach for extracting informative content from web pages

Cited by 47 publications

References 23 publications

A hybrid FAM‐CART model for online data classification

A hybrid FAM‐CART model for online data classification

The Effect of Feature Selection on Phish Website Detection

An intelligent Mobile Web Browser to adapt the mobile web as a function of the physical environment

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