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Two Stages Feature Selection Based on Filter Ranking Methods and SVMRFE on Medical Applications

  • Hayet DjellaliEmail author
  • Nacira Ghoualmi Zine
  • Nabiha Azizi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 1)

Abstract

This paper investigates feature selection stage applied to medical classification of disease on datasets from UCI repository. Feature selection methods based on minimum Redundancy Maximum Relevance (mRMR) filter and Ficher score were applied, each of them select a subset of features then the selection criteria is used to get the initial features subset. The second stage Support vector machine recursive feature elimination is performed to have the final subset. Experiments show that the proposed method provide an accuracy of 99.89 % on hepatitis dataset and 97.81 % on Wisconcin Breast cancer dataset and outperforms MRMR and Support vector machine recursive feature elimination SVM-RFE methods, as well as other popular methods on UCI database, and select features that are relevant in discriminating cancer class (malign/benign).

Keywords

Support Vector Machine Feature Selection Support Vector Machine Classifier Feature Subset Feature Selection Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hayet Djellali
    • 1
    • 2
    • 4
    Email author
  • Nacira Ghoualmi Zine
    • 1
    • 2
    • 4
  • Nabiha Azizi
    • 3
    • 4
  1. 1.Computer Science DepartmentAnnabaAlgeria
  2. 2.LRS LaboratoryAnnabaAlgeria
  3. 3.Labged LaboratoryAnnabaAlgeria
  4. 4.Badji Mokhtar UniversityAnnabaAlgeria

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