Feature Selection for SVM-Based Vascular Anomaly Detection

  • Maria A. Zuluaga
  • Edgar J. F. Delgado Leyton
  • Marcela Hernández Hoyos
  • Maciej Orkisz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6533)


This work explores feature selection to improve the performance in the vascular anomaly detection domain. Starting from a previously defined classification framework based on Support Vector Machines (SVM), we attempt to determine features that improve classification performance and to define guidelines for feature selection. Three different strategies were used in the feature selection stage, while a Density Level Detection-SVM (DLD-SVM) was used to validate the performance of the selected features over testing data. Results show that a careful feature selection results in a good classification performance. DLD-SVM shows a poor performance when using all the features together, owing to the curse of dimensionality.


Support Vector Machine Feature Selection Random Forest Concentric Ring Inertia Moment 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Maria A. Zuluaga
    • 1
    • 2
  • Edgar J. F. Delgado Leyton
    • 1
    • 2
  • Marcela Hernández Hoyos
    • 1
  • Maciej Orkisz
    • 2
  1. 1.Grupo Imagine, Grupo de Ingeniería BiomédicaUniversidad de los AndesBogotáColombia
  2. 2.CNRS UMR5220; INSERM U630CREATIS; Université de Lyon; Université Lyon 1; INSA-LyonVilleurbanneFrance

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