A high performance centroid-based classification approach for language identification

Takci H., Gungor T.

PATTERN RECOGNITION LETTERS, cilt.33, sa.16, ss.2077-2084, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 33 Sayı: 16
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1016/j.patrec.2012.06.012
  • Dergi Adı: PATTERN RECOGNITION LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2077-2084
  • Anahtar Kelimeler: Language identification, Centroid-based classification, IDF (inverse document frequency), ICF (inverse class frequency), TEXT, SUPPORT
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

Centroid-based classification is a machine learning approach used in the text classification domain. The main advantage of centroid-based classifiers is their high performance during both the training stage and the classification stage. However, the success rate can be lower than the other classifiers if good centroid values are not used. In this paper, we apply the centroid-based classification method to the language identification problem, which can be considered as a sub-problem of text classification. We propose a novel method named as inverse class frequency to increase the quality of the centroid values, which involves an update of the classical values. We also use a feature set formed of individual characters rather than words or n-gram sequences to decrease the training and classification times. The experiments were performed on the ECI/MCI corpus and the method was compared with other methods and previous studies. The results showed that the proposed approach yields high success rates and works very efficiently for language identification. (c) 2012 Elsevier B.V. All rights reserved.