Ranking importance based information on the world wide web

Akshay Kumar Maan, Alex Pappachen James

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Identifying useful features for classification and forecast tasks from a ranked data is highly difficult and challenging. By ranking user popularity ratings from normalised area histograms, a method of feature selection for ranked data inspired from the law of vital few is proposed. We propose that the attributes that are most stable against the variations in classes have their usefulness in a forecasting task, while the attributes that are most unstable between inter-class samples but most stable within intra-class samples have their usefulness in classification tasks. The performance of the proposed method is demonstrated through a realistic example of web-content data from Yahoo! research repository: the user rating of web pages. The attributes in the data when ranked based on their importance in a year show distinct characteristics of performance in the tasks of popularity forecast and classification.

Original languageEnglish
Title of host publicationProceedings of the 2012 International Conference on Advances in Computing, Communications and Informatics, ICACCI'12
Pages889-897
Number of pages9
DOIs
Publication statusPublished - Sep 17 2012
Externally publishedYes
Event2012 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2012 - Chennai, India
Duration: Aug 3 2012Aug 5 2012

Publication series

NameACM International Conference Proceeding Series

Other

Other2012 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2012
CountryIndia
CityChennai
Period8/3/128/5/12

Keywords

  • WWW
  • feature selection
  • ranked distribution
  • user rating

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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