Binary black hole algorithm for feature selection and classification on biological data


PASHAEI E., AYDIN N.

APPLIED SOFT COMPUTING, cilt.56, ss.94-106, 2017

  • Cilt numarası: 56
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1016/j.asoc.2017.03.002
  • Dergi Adı: APPLIED SOFT COMPUTING
  • Sayfa Sayısı: ss.94-106

Özet

data often consist of redundant and irrelevant features. These features can lead to misleading in modeling the algorithms and overfitting problem. Without a feature selection method, it is difficult for the existing models to accurately capture the patterns on data. The aim of feature selection is to choose a small number of relevant or significant features to enhance the performance of the classification. Existing feature selection methods suffer from the problems such as becoming stuck in local optima and being computationally expensive. To solve these problems, an efficient global search technique is needed.