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5 - Multivariate Feature Selection

from Part I - Framework for Multivariate Biomarker Discovery

Published online by Cambridge University Press:  30 May 2024

Darius M. Dziuda
Affiliation:
Central Connecticut State University
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Summary

Chapter 5 is dedicated to the most important part of predictive modeling for biomarker discovery based on high-dimensional data – multivariate feature selection. When dealing with sparse biomedical data whose dimensionality is much higher than the number of training observations, the crucial issue is to overcome the curse of dimensionality by using methods capable of elevating signal (predictive information) from the overwhelming noise. One way of doing this is to perform many (hundreds or thousands) parallel feature selection experiments based on different random subsamples of the original training data and then aggregating their results (for example, by analyzing the distribution of variables among the results of those parallel experiments). Two designs of such parallel feature selection experiments are discussed in detail: one based on recursive feature elimination, and the other on implementing the stepwise hybrid selection with T2. The chapter includes also descriptions of three evolutionary feature selection algorithms: simulated annealing, genetic algorithms, and particle swarm optimization.

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Chapter
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Multivariate Biomarker Discovery
Data Science Methods for Efficient Analysis of High-Dimensional Biomedical Data
, pp. 76 - 98
Publisher: Cambridge University Press
Print publication year: 2024

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