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New Method for Instance Feature Selection Using Redundant Features for Biological Data

  • Waad BouaguelEmail author
  • Emna Mouelhi
  • Ghazi Bel Mufti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9582)

Abstract

Biological data bases are characterized by a very large number of features and a few instances which make classification more difficult and time consuming. This problem can be solved using feature selection approach. The Filter feature selection method ranks features according to their significance level. Then it selects the most significant features and discards the rest. The discarded features may provide some useful information and could be useful to further consideration. Hence, we propose a new feature selection method that uses these eliminated features in order to increase the classification performance and avoid the curse of dimensionality. The new approach is based on the idea of transforming the value of the similar features into new instances for the retained features. We aim to reduce the feature space by performing features selection and increasing the learning space in creating new instances using the redundant features.

Keywords

Curse of dimensionality Relief Feature selection Filter 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.LARODEC, ISGUniversity of TunisTunisTunisia
  2. 2.ISGUniversity of TunisTunisTunisia
  3. 3.LARIME, ESSECUniversity of TunisTunisTunisia

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