Improving the Diagnosis of Breast Cancer Using Regularized Logistic Regression with Adaptive Elastic Net

Logistic Regression with Group `1 vs. Elastic Net Regularization

Bayesian Quantile Regression with Adaptive Elastic Net Penalty for Longitudinal Data

Longitudinal studies include the important parts of epidemiological surveys, clinical trials and social studies. In longitudinal studies, measurement of the responses is conducted repeatedly through time. Often, the main goal is to characterize the change in responses over time and the factors that influence the change. Recently, to analyze this kind of data, quantile regression has been taken ...

Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions

BACKGROUND Genomic selection (GS) is emerging as an efficient and cost-effective method for estimating breeding values using molecular markers distributed over the entire genome. In essence, it involves estimating the simultaneous effects of all genes or chromosomal segments and combining the estimates to predict the total genomic breeding value (GEBV). Accurate prediction of GEBVs is a central...

Aligned Graph Classification with Regularized Logistic Regression

Data with intrinsic feature relationships are becoming abundant in many applications including bioinformatics and sensor network analysis. In this paper we consider a classification problem where there is a fixed and known binary relation defined on the features of a set of multivariate random variables. We formalize such a problem as an aligned graph classification problem. By incorporating th...

Music Tagging with Regularized Logistic Regression

In this paper, we present a set of simple and efficient regularized logistic regression algorithms to predict tags of music. We first vector-quantize the delta MFCC features using k-means and construct “bag-of-words” representation for each song. We then learn the parameters of these logistic regression algorithms from the “bag-of-words” vectors and ground truth labels in the training set. At t...