Support Vector Machine Based Classification of Clicked Document Using Topic Ontology for Profile Generation

Prabaharan, S. Beski; Wahidabanu, R. S. D.

doi:10.3923/itj.2012.1007.1015

Cited by 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional methods() for text clustering, classifying, searching, and analyzing are unable to locate accurately the significance of words in a specific period. These methods neglect the relation between the changing of frequency of some words and time change.…”

Section: Introductionmentioning

confidence: 99%

A new retrieval method based on time series variation using field association terms

Atlam

Ghaleb

Taha

et al. 2017

Math Methods in App Sciences

View full text Add to dashboard Cite

This paper studies the effects of the time change on the frequencies of specific terms connected to the document field in a given period. These specific terms are the field association (FA) terms. The paper also suggests a new method for automatic evaluation of the stabilization classes of FA terms to improve the precision of decision tree. The stabilization classes point out the popularity of list of FA terms depending on time change. Moreover, the suggested method manipulates the problem of the scattering of data numbers among classes to improve the performance of decision tree precision. The presented method is evaluated through conducting experiments by simulating the result of 1245 files, which are equivalent to 4.15 MB. The F-measure for increment, fairly steady, and decrement classes achieves %90.4, %99.3, and %38.6, sequentially.

show abstract

Section: Introductionmentioning

confidence: 99%

A new retrieval method based on time series variation using field association terms

Atlam

Ghaleb

Taha

et al. 2017

Math Methods in App Sciences

View full text Add to dashboard Cite

show abstract

A New Approach of Time Series Variation Based on Power Links and Field Association Words

Malki¹,

Atlam²,

Noor³

et al. 2020

JCC

View full text Add to dashboard Cite

This paper has proposed a new methodology extracting stability classes of field association words depending on automatically power link analysis to enhance the precision of decision tree. In this paper, we have studied the effects of the time variation based on the frequencies of specific words called field association words that connected to documents using power link in a specific period. The stability classes have referred to the popularity of field association words based on the change of time in a given period. The new approach has evaluated by conducting experiments simulating results of 1575 files (about 5.16 MB). Based on these experiments, it has turned out that, the F-measure for ascending, stable and descending classes have achieved 93.6%, 99.8% and 75.7%, respectively. These results mean that F-measure was increasing by 12%, 4% and 34% than traditional methods because of the power link analysis.

show abstract

Support Vector Machine Based Classification of Clicked Document Using Topic Ontology for Profile Generation

Cited by 2 publications

References 7 publications

A new retrieval method based on time series variation using field association terms

A new retrieval method based on time series variation using field association terms

A New Approach of Time Series Variation Based on Power Links and Field Association Words

Contact Info

Product

Resources

About