Hierarchical feature selection for biological data

Witold Jacak, Karin Pröll

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review


In this paper we present feature selection in biological data by combining unsupervised learning with supervised cross validation. Unsupervised clustering methods are used to perform a clustering of object-data for a chosen subset of input features and given number of clusters. The resulting object clusters are compared with the predefined original object classes and a matching factor (score) is calculated. This score is used as criterion function for heuristic sequential feature selection and a cross selection algorithm.
Original languageEnglish
Title of host publication26th European Modeling and Simulation Symposium, EMSS 2014
EditorsYuri Merkuryev, Lin Zhang, Emilio Jimenez, Francesco Longo, Michael Affenzeller, Agostino G. Bruzzone
PublisherDIPTEM University of Genova
Number of pages5
ISBN (Electronic)9788897999324
ISBN (Print)978-88-97999-38-6
Publication statusPublished - 2014
EventThe 26th European Modeling & Simulation Symposium EMSS 2014 - Bordeaux, France
Duration: 10 Sept 201412 Sept 2014

Publication series

Name26th European Modeling and Simulation Symposium, EMSS 2014


ConferenceThe 26th European Modeling & Simulation Symposium EMSS 2014
Internet address


  • classification
  • clustering
  • feature selection
  • sequential feature selection
  • Sequential feature selection
  • Feature selection
  • Classification
  • Clustering


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