Two step feature selection: Approximate functional dependency approach using membership values

Uncu O., Turksen I.

Annual IEEE International Conference on Fuzzy Systems, Budapest, Macaristan, 25 - 29 Temmuz 2004, ss.1643-1648 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/fuzzy.2004.1375427
  • Basıldığı Şehir: Budapest
  • Basıldığı Ülke: Macaristan
  • Sayfa Sayıları: ss.1643-1648
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

Feature selection is one of the most important issues in fields such as system modelling and pattern recognition. In this study, a new feature selection algorithm that combines feature wrapper and feature filter approaches is proposed in order to identify the significant input variables in systems with continuous domains. The proposed method utilizes functional dependency concept and K-Nearest Neighbourhood method to implement the feature filter and feature wrapper, respectively. It is common to have outliers and noise in real-life data. In order to make the proposed feature selection algorithm noise and outlier resistant, approximate functional dependencies are used by utilizing membership values that inherently cope with uncertainty in the data.