Incremental conic functions algorithm for large scale classification problems

ÇİMEN E., ÖZTÜRK G., GEREK Ö. N.

DIGITAL SIGNAL PROCESSING, cilt.77, ss.187-194, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 77
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1016/j.dsp.2017.11.010
  • Dergi Adı: DIGITAL SIGNAL PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.187-194
  • Anahtar Kelimeler: Polyhedral conic functions, Mathematical programming, Classification, Machine learning, SEPARATION, SEPARABILITY
  • Anadolu Üniversitesi Adresli: Evet

Özet

In order to cope with classification problems involving large datasets, we propose a new mathematical programming algorithm by extending the clustering based polyhedral conic functions approach. Despite the high classification efficiency of polyhedral conic functions, the realization previously required a nested implementation of k-means and conic function generation, which has a computational load related to the number of data points. In the proposed algorithm, an efficient data reduction method is employed to the k-means phase prior to the conic function generation step. The new method not only improves the computational efficiency of the successful conic function classifier, but also helps avoiding model over-fitting by giving fewer (but more representative) conic functions. (C) 2017 Elsevier Inc. All rights reserved.