Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data

Authors:
Yongdai Kim;Sunghoon Kwon;Seuck Heun Song
Affiliations:
Seoul National University, Korea;Seoul National University, Korea;Korea University, Korea
Venue:
Computational Statistics & Data Analysis
Year:
2006

Citing 4
Cited 7

Better subset regression using the nonnegative garrote

Technometrics
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds

IEEE Transactions on Pattern Analysis and Machine Intelligence
How accurately can we control the FDR in analyzing microarray data?

Bioinformatics

Efficient methods for estimating constrained parameters with applications to regularized (lasso) logistic regression

Computational Statistics & Data Analysis
Favorability functions based on kernel density estimation for logistic models: A case study

Computational Statistics & Data Analysis
Adaptive clustering for time series: Application for identifying cell cycle expressed genes

Computational Statistics & Data Analysis
Multinomial logistic regression and product unit neural network models: Application of a new hybrid methodology for solving a classification problem in the livestock sector

Expert Systems with Applications: An International Journal
A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples

Computational Statistics & Data Analysis
Polarization of forecast densities: A new approach to time series classification

Computational Statistics & Data Analysis
Sparse group lasso and high dimensional multinomial classification

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

Monitoring gene expression profiles is a novel approach to cancer diagnosis. Several studies have showed that the sparse logistic regression is a useful classification method for gene expression data. Not only does it give a sparse solution with high accuracy, it provides the user with explicit probabilities of classification apart from the class information. However, its optimal extension to more than two classes is not obvious. In this paper, we propose a multiclass extension of sparse logistic regression. Analysis of five publicly available gene expression data sets shows that the proposed method outperforms the standard multinomial logistic model in prediction accuracy as well as gene selectivity.