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Neural Networks Based Feature Selection in Biological Data Analysis

  • Witold Jacak
  • Karin Pröll
  • Stephan Winkler
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 6)

Abstract

In this chapter we present a novel method for scoring function specification and feature selection by combining unsupervised learning with supervised cross validation. Various clustering algorithms such as one dimensional Kohonen SOM, k-means, fuzzy c-means and hierarchical clustering procedures are used to perform a clustering of object-data for a chosen subset of input features and a given number of clusters. The resulting object clusters are compared with the predefined target classes and a matching factor (score) is calculated. This score is used as criterion function for heuristic sequential and cross feature selection.

Keywords

Feature Selection Feature Selection Method Unsupervised Learning Recognition Quality Target Class 
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 2014

Authors and Affiliations

  1. 1.Dept. of Software Engineering at HagenbergUpper Austrian University of Applied SciencesHagenbergAustria

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