A Two-Level Approach based on Integration of Bagging and Voting for Outlier Detection

Doğan A., Birant D.

JOURNAL OF DATA AND INFORMATION SCIENCE, cilt.5, sa.2, ss.111-135, 2020 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 5 Sayı: 2
  • Basım Tarihi: 2020
  • Doi Numarası: 10.2478/jdis-2020-0014
  • Dergi Adı: JOURNAL OF DATA AND INFORMATION SCIENCE
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Directory of Open Access Journals
  • Sayfa Sayıları: ss.111-135
  • Anahtar Kelimeler: Outlier detection, Local outlier factor, Ensemble learning, Bagging, Voting, SUBSPACES
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Purpose: The main aim of this study is to build a robust novel approach that is able to detect outliers in the datasets accurately. To serve this purpose, a novel approach is introduced to determine the likelihood of an object to be extremely different from the general behavior of the entire dataset.