Consensus Feature Ranking in Datasets with Missing Values

  • Authors:
  • Shobeir Fakhraei;Hamid Soltanian-Zadeh;Farshad Fotouhi;Kost Elisevich

  • Affiliations:
  • -;-;-;-

  • Venue:
  • ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
  • Year:
  • 2010

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Abstract

Development of a feature ranking method based upon the discriminative power of features and unbiased towards classifiers is of interest. We have studied a consensus feature ranking method, based on multiple classifiers, and have shown its superiority to well known statistical ranking methods. In a target environment such as a medical dataset, missing values and an unbalanced distribution of data must be taken into consideration in the ranking and evaluation phases in order to legitimately apply a feature ranking method. In a comparison study, a Performance Index (PI) is proposed that takes into account both the number of features and the number of samples involved in the classification.