Learning the stress function pattern of ordered weighted average aggregation using DBSCAN clustering

NASİBOĞLU R., TEZEL B. T., Nasibov E.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, cilt.34, sa.3, ss.477-492, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34 Sayı: 3
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1002/int.22061
  • Dergi Adı: INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.477-492
  • Anahtar Kelimeler: aggregation, ordered weighted averaging (OWA) operators, stress function, clustering, density-based clustering, OWA OPERATOR, MINIMAX DISPARITY
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Ordered weighted average (OWA) operator provides a parameterized class of mean type operators between the minimum and the maximum. It is an important tool that can reflect the strategy of a decision maker for decision-making problems. In this study, the idea of obtaining the stress function from OWA weights has been put forward to generalize and characterize OWA weights. The main idea in this paper is mainly constructed on the basis that, generally, stress functions can be constructed using a mixture of constant and linear components. So, we can consider the stress function as a piecewise linear function. For obtaining stress functions as piecewise linear functions, we present a clustering-based approach for OWA weight generalization. This generalization is made using the DBSCAN algorithm as the learning method of a stress function associated with known OWA weights. In the learning process, the whole data set is divided into clusters, and then linear functions are obtained via a least squares estimator.