The Mahalanobis Distance for Feature Selection Using Genetic Algorithms: An Application to BCI

  • Maria Elena Bruni
  • D. Nguyen Duy
  • Patrizia Beraldi
  • Antonio Violi
Part of the AIRO Springer Series book series (AIROSS, volume 1)


High dimensionality is a big problem that has been receiving a lot of interest from data scientists. Classification algorithms usually have trouble handling high dimensional data, and Support Vector Machine is not an exception. Trying to reduce the dimensionality of data selecting a subset of the original features is a solution to this problem. Many proposals have been applied and obtained positive results, including the use of Genetic Algorithms that has been proven to be an effective strategy. In this paper, a new method using Mahalanobis distance as a fitness function is introduced. The performance of the proposed method is investigated and compared with the state-of-the-art methods.


Mahalanobis distance Genetic algorithm Feature selection 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Maria Elena Bruni
    • 1
  • D. Nguyen Duy
    • 2
  • Patrizia Beraldi
    • 1
  • Antonio Violi
    • 1
  1. 1.Department of Mechanical, Energy and Management EngineeringUniversity of CalabriaCosenza, RendeItaly
  2. 2.Department of Mathematics and Computer ScienceUniversity of CalabriaCosenza, RendeItaly

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