Step down logistic regression for feature selection

Baykal N.

International Conference on Applied Statistical in Medical Sciences, Ankara, Turkey, 01 August 1997, vol.4, pp.121-131 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 4
  • City: Ankara
  • Country: Turkey
  • Page Numbers: pp.121-131
  • Middle East Technical University Affiliated: Yes


This paper proposes a methodology to the feature selection problem of pattern classification problems. For this purpose, pattern recognition or signal processing involves two major tasks: clustering transformation and then, feature selection. The concept of clustering reduces the dimensionality of the measurement space and generates a set of features. However, there is so far no covering theory how to select discriminative and biologically important features from the pool of generated features. This paper describes a hybrid approach in which an unsupervised learning method namely, a modified Self-Organizing Feature Map is used for feature extraction and then, a smaller linearly independent set of features are selected using Step-Down Logistic Regression. Experimental results using Doppler umbilical artery waveforms indicate that classification results using such features shows a very high correlation with the actual output. Also, Modified Self-Organizing Feature Map followed with the Step-Down Logistic Regression show to be a very powerful approach to discovering useful feature space in complex data.