Hybridized term-weighting method for Dark Web classification

Sabbah, Thabit; Selamat, Ali; Selamat, Md. Hafiz; Ibrahim, Roliana; Fujita, Hamido

doi:10.1016/j.neucom.2015.09.063

Cited by 50 publications

(25 citation statements)

References 79 publications

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“…For TC, we assume that the document d k consisting of a number of term is written as d k = ( t 1 , t 2 , …, t n ). The probability that a document d k belongs to the category c i can be defined by Equation . For more details, you can refer to Farid et al, which gives a complete description of the theory of NB.…”

Section: Methodsmentioning

confidence: 99%

“…The probability that a document d k belongs to the category c i can be defined by Equation (15). 9,21 For more details, you can refer to Farid et al, 60 which gives a complete description of the theory of NB.…”

Section: Naïve Bayesmentioning

confidence: 99%

“…Zheng et al built a flexible and easily extensible framework for term representation to unify various Twitter‐specific features. Sabbah et al developed a hybridized approach that utilizes the use of a wider range of term weighting schemes simultaneously such as TF, DF, TF‐IDF, entropy, and so on. Since then, four modified frequency‐based term weighting schemes that consider missing terms when computing the weights of existing terms are proposed .…”

Section: Introductionmentioning

confidence: 99%

“…Ay Karakuş et al 7 introduced a binary classification study for the Turkish language on movie reviews. In addition, text categorization has multiple applications, such as web page classification, 8,9 spam detection, 10,11 author identification, 12,13 and customer relationship management. 14 TC is such a task to categorize unlabelled natural language texts into a predefined set of thematic classes based on their content.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An improved term weighting scheme for text classification

Zhong

2019

Concurrency and Computation

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Summary Text representation is a necessary and primary procedure in performing text classification (TC), which first needs to be obtained through an information‐rich term weighting scheme to achieve higher TC performance. So far, term frequency‐inverse document frequency (TF‐IDF) is the most widely used term weighting scheme, but it suffers from two deficiencies. First, the global weighting factors IDF in TF‐IDF approaches infinity if a certain term does not occur in a text. Second, the IDF is equal to zero if a certain term appears in any text. To offset these drawbacks, we first conduct an in‐depth analysis of the current term weighting schemes, and subsequently, an improved term weighting scheme called term frequency‐inverse exponential frequency (TF‐IEF) and its various variants are proposed. The proposed method replaces IDF with the new global weighting factor IEF to characterize the global weighting factor log‐like IDF in the corpus, which can greatly reduce the effect of feature (term) with high local weighting factor TF in term weighting. As a result, a more representative feature can be generated. We carried out a series of experiments on two commonly used data sets (corpora) utilizing Naïve Bayes and support vector machine classifiers to validate the performance of our proposed schemes. Experimental results explicitly reveal that the proposed term weighting schemes come with better performance than the compared schemes.

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Section: Methodsmentioning

confidence: 99%

Section: Naïve Bayesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An improved term weighting scheme for text classification

Zhong

2019

Concurrency and Computation

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“…The hybridized method combines feature sets generated by the term weighting schemes TF, DF, TF-IDF, Glasgow and Entropy into one feature set for effective classification. Improved Web Page Identification Method using Neural Networks proposed by Ali Selamat et al [8] is based on the improvement of feature selection of the web pages using Class Based Feature Vectors. The approach has been examined using the modified term weighting scheme.…”

Section: Introductionmentioning

confidence: 99%

Improved Term Weighting Technique for Automatic Web Page Classification

Kathirvalavakumar¹,

Kanagavel²

2016

JILSA

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Automatic web page classification has become inevitable for web directories due to the multitude of web pages in the World Wide Web. In this paper an improved Term Weighting technique is proposed for automatic and effective classification of web pages. The web documents are represented as set of features. The proposed method selects and extracts the most prominent features reducing the high dimensionality problem of classifier. The proper selection of features among the large set improves the performance of the classifier. The proposed algorithm is implemented and tested on a benchmarked dataset. The results show the better performance than most of the existing term weighting techniques.

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