EDA-Based Logistic Regression Applied to Biomarkers Selection in Breast Cancer

Authors:
Santiago González;Victor Robles;Jose Maria Peña;Oscar Cubo
Affiliations:
Department of Computer Architecture, Universidad Politécnica de Madrid, Madrid, Spain;Department of Computer Architecture, Universidad Politécnica de Madrid, Madrid, Spain;Department of Computer Architecture, Universidad Politécnica de Madrid, Madrid, Spain;Department of Computer Architecture, Universidad Politécnica de Madrid, Madrid, Spain
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Year:
2009

Citing 7
Cited 0

Feature subset selection by Bayesian network-based optimization

Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Combinatonal Optimization by Learning and Simulation of Bayesian Networks

UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Dimension Reduction-Based Penalized Logistic Regression for Cancer Classification Using Microarray Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)

Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
The curse of dimensionality in data mining and time series prediction

IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Logistic regression (LR) is a simple and efficient supervised learning algorithm for estimating the probability of an outcome variable. This algorithm is widely accepted and used in medicine for classification of diseases using DNA microarray data. Classical LR does not perform well for microarrays when applied directly, because the number of variables exceeds the number of samples. However, by reducing the number of genes and selecting specific variables (using filtering methods) great results can be obtained with this algorithm. On this contribution we propose a novel approach for fitting the (penalized) LR models based on EDAs. Breast Cancer dataset has been proposed to compare both accuracy and gene selection.