A Novel Approach for Panel Data: An Ensemble of Weighted Functional Margin SVM Models

EYGİ ERDOĞAN B., AKYÜZ S., Karadayı Ataş P.

Information Sciences, cilt.557, ss.373-381, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 557
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.ins.2019.02.045
  • Dergi Adı: Information Sciences
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Computer & Applied Sciences, INSPEC, Library, Information Science & Technology Abstracts (LISTA), Metadex, MLA - Modern Language Association Database, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.373-381
  • Anahtar Kelimeler: Bank bankruptcy, Ensemble learning, Panel data, Support vector machines (SVM), Generalized linear model, Functional margin
  • Marmara Üniversitesi Adresli: Evet

Özet

Ensemble machine learning methods are frequently used for classification problems and it is known that they may boost the prediction accuracy. Support Vector Machines are widely used as base classifiers during the construction of different types of ensembles. In this study, we have constructed a weighted functional margin classifier ensemble on panel financial ratios to discriminate between solid and unhealthy banks for Turkish commercial bank case. We proposed a novel ensemble generation method enhanced by a pruning strategy to increase the prediction performance and developed a novel aggregation approach for ensemble learning by using the idea of weighted sums. The prediction performances are compared with a panel logistic regression which is considered a benchmark method for panel data. The results show that the proposed ensemble method is more successful than the straight SVM and the classical generalized linear model approach. (C) 2019 Elsevier Inc. All rights reserved.