A novel SMOTE-based resampling technique trough noise detection and the boosting procedure

Sağlam F., Cengiz M. A.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.200, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 200
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.eswa.2022.117023
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Anahtar Kelimeler: Oversampling, SMOTE, Class imbalance, Noisy data, SAMPLING METHOD, CLASSIFIERS, PREDICTION, MAJORITY
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

Most of the classification methods assume that the numbers of class observations are balanced. In such cases, models are predicted by giving biased weight to the the class with more observations. Therefore, the classifiers ignore the class with smaller number of observations and the majority class makes biased predictions. There are some advised performance measures to be used in datasets, as well as recommended approaches to solve class imbalance problem. One of the most widely used methods is resampling method. In this study, the difficulties relevant to random oversampling (ROS) and synthetic minority oversampling technique (SMOTE), which are some of the oversampling methods, are discussed. This study aims to propose a combination of a new noise detection method and SMOTE to overcome those difficulties. Using the boosting procedure in ensemble algo-rithms, noise detection is possible with the proposed SMOTE with boosting (SMOTEWB) method, which makes use of this information to determine the appropriate number of neighbors for each observation within SMOTE algorithm.