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Step down logistic regression for feature selection

1997-08-01
Baykal, Nazife
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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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.
Feature extraction, Pattern classifier, Self-organizing feature map, Likelihood ratio test, Step-down logistic regression
https://hdl.handle.net/11511/52945
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