Unsupervised Neural Networks based Scoring and Feature Selection in Biological Data Analysis

Witold Jacak, Karin Pröll

Research output: Chapter in Book/Report/Conference proceedingsConference contribution

Abstract

In this paper we present a novel method for scoring function specification and feature selection by combining unsupervised learning with supervised cross validation. A one dimensional Kohonen SOM is 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.
Original languageEnglish
Title of host publicationProceedings of IEEE APCAST'12 Conference
Pages18-23
Publication statusPublished - 2012
EventIEEE APCast'12 - Sydney, Australia
Duration: 6 Feb 20128 Feb 2012

Conference

ConferenceIEEE APCast'12
CountryAustralia
CitySydney
Period06.02.201208.02.2012

Keywords

  • classification
  • clustering
  • feature selection
  • Kohonen SOM
  • MLP

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