Title of article :
High Relevance Keyword Extraction facility for Bayesian text classification on different domains of varying characteristic
Author/Authors :
Lee، نويسنده , , Lam Hong and Isa، نويسنده , , Dino and Choo، نويسنده , , Wou Onn and Chue، نويسنده , , Wen Yeen، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Abstract :
High Relevance Keyword Extraction (HRKE) facility is introduced to Bayesian text classification to perform feature/keyword extraction during the classifying stage, without needing extensive pre-classification processes. In order to perform the task of keyword extraction, HRKE facility uses the posterior probability value of keywords within a specific category associated with text document. The experimental results show that HRKE facility is able to ensure promising classification performance for Bayesian classifier while dealing with different text classification domains of varying characteristics. This method guarantees an effective and efficient Bayesian text classifier which is able to handle different domains of varying characteristics, with high accuracy while maintaining the simplicity and low cost processes of the conventional Bayesian classification approach.
Keywords :
Pattern recognition , feature selection , Machine Learning , Bayesian probabilistic algorithm , Bayes formula , High Relevance Keyword Extraction
Journal title :
Expert Systems with Applications
Journal title :
Expert Systems with Applications