Search (2 results, page 1 of 1)

  • × author_ss:"Ko, Y."
  • × language_ss:"e"
  • × theme_ss:"Automatisches Klassifizieren"
  1. Ko, Y.: ¬A new term-weighting scheme for text classification using the odds of positive and negative class probabilities (2015) 0.01 
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    Abstract
    Text classification (TC) is a core technique for text mining and information retrieval. It has been applied to many applications in many different research and industrial areas. Term-weighting schemes assign an appropriate weight to each term to obtain a high TC performance. Although term weighting is one of the important modules for TC and TC has different peculiarities from those in information retrieval, many term-weighting schemes used in information retrieval, such as term frequency-inverse document frequency (tf-idf), have been used in TC in the same manner. The peculiarity of TC that differs most from information retrieval is the existence of class information. This article proposes a new term-weighting scheme that uses class information using positive and negative class distributions. As a result, the proposed scheme, log tf-TRR, consistently performs better than do other schemes using class information as well as traditional schemes such as tf-idf.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2553-2565
  2. Ko, Y.; Seo, J.: Text classification from unlabeled documents with bootstrapping and feature projection techniques (2009) 0.00 
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    Source
    Information processing and management. 45(2009) no.1, S.70-83

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