Undesirable effects of output normalization in multiple classifier systems

Altincay H., Demirekler M.

PATTERN RECOGNITION LETTERS, cilt.24, ss.1163-1170, 2003 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24
  • Basım Tarihi: 2003
  • Doi Numarası: 10.1016/s0167-8655(02)00286-6
  • Dergi Adı: PATTERN RECOGNITION LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1163-1170
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

Incomparability of the classifier output scores is a major problem in the combination of different classification systems. In order to deal with this problem, the measurement level classifier outputs are generally normalized. However, empirical results have shown that output normalization may lead to some undesirable effects. This paper presents analyses for some most frequently used normalization methods and it is shown that the main reason for these undesirable effects of output normalization is the dimensionality reduction in the output space. An artificial classifier combination example and a real-data experiment are provided where these effects are further clarified. (C) 2002 Elsevier Science B.V. All rights reserved.