Improving Naive Bayes with Online Feature Selection for Quick Adaptation to Evolving Feature Usefulness

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The definition of what makes an article interesting varies from user to user and continually evolves even for a single user. As a result, for news recommendation systems, useless document features can not be determined a priori and all features are usually considered for interestingness classification. Consequently, the presence of currently useless features degrades classification performance [1], particularly over the initial set of news articles being classified. The initial set of document is critical for a user when considering which particular news recommendation system to adopt. To address these problems, we introduce an improved version of the naive Bayes classifier ... continued below

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Pon, R K; Cardenas, A F & Buttler, D J September 19, 2007.

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The definition of what makes an article interesting varies from user to user and continually evolves even for a single user. As a result, for news recommendation systems, useless document features can not be determined a priori and all features are usually considered for interestingness classification. Consequently, the presence of currently useless features degrades classification performance [1], particularly over the initial set of news articles being classified. The initial set of document is critical for a user when considering which particular news recommendation system to adopt. To address these problems, we introduce an improved version of the naive Bayes classifier with online feature selection. We use correlation to determine the utility of each feature and take advantage of the conditional independence assumption used by naive Bayes for online feature selection and classification. The augmented naive Bayes classifier performs 28% better than the traditional naive Bayes classifier in recommending news articles from the Yahoo! RSS feeds.

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PDF-file: 8 pages; size: 0.6 Mbytes

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  • Presented at: SIAM Conference on Data Mining 2008, Atlanta, GA, United States, Apr 24 - Apr 26, 2008

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  • Report No.: UCRL-CONF-235295
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 929189
  • Archival Resource Key: ark:/67531/metadc896317

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • September 19, 2007

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  • Sept. 27, 2016, 1:39 a.m.

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  • Dec. 5, 2016, 8:50 p.m.

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Pon, R K; Cardenas, A F & Buttler, D J. Improving Naive Bayes with Online Feature Selection for Quick Adaptation to Evolving Feature Usefulness, article, September 19, 2007; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc896317/: accessed December 13, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.