Next Article in Journal
The Lorenz Curve: A Proper Framework to Define Satisfactory Measures of Symbol Dominance, Symbol Diversity, and Information Entropy
Next Article in Special Issue
Multivariate Tail Coefficients: Properties and Estimation
Previous Article in Journal
Statistical Uncertainties of Space Plasma Properties Described by Kappa Distributions
Previous Article in Special Issue
Towards a Unified Theory of Learning and Information
 
 
Article

Prediction and Variable Selection in High-Dimensional Misspecified Binary Classification

by 1,*,† and 2,†
1
Institute of Information Technology, Warsaw University of Life Sciences (SGGW), Nowoursynowska 159, 02-776 Warszawa, Poland
2
Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Chopina 12/18, 87-100 Toruń, Poland
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Entropy 2020, 22(5), 543; https://doi.org/10.3390/e22050543
Received: 20 April 2020 / Revised: 8 May 2020 / Accepted: 11 May 2020 / Published: 13 May 2020
In this paper, we consider prediction and variable selection in the misspecified binary classification models under the high-dimensional scenario. We focus on two approaches to classification, which are computationally efficient, but lead to model misspecification. The first one is to apply penalized logistic regression to the classification data, which possibly do not follow the logistic model. The second method is even more radical: we just treat class labels of objects as they were numbers and apply penalized linear regression. In this paper, we investigate thoroughly these two approaches and provide conditions, which guarantee that they are successful in prediction and variable selection. Our results hold even if the number of predictors is much larger than the sample size. The paper is completed by the experimental results. View Full-Text
Keywords: misclassification risk; model misspecification; penalized estimation; supervised classification; variable selection consistency misclassification risk; model misspecification; penalized estimation; supervised classification; variable selection consistency
MDPI and ACS Style

Furmańczyk, K.; Rejchel, W. Prediction and Variable Selection in High-Dimensional Misspecified Binary Classification. Entropy 2020, 22, 543. https://doi.org/10.3390/e22050543

AMA Style

Furmańczyk K, Rejchel W. Prediction and Variable Selection in High-Dimensional Misspecified Binary Classification. Entropy. 2020; 22(5):543. https://doi.org/10.3390/e22050543

Chicago/Turabian Style

Furmańczyk, Konrad, and Wojciech Rejchel. 2020. "Prediction and Variable Selection in High-Dimensional Misspecified Binary Classification" Entropy 22, no. 5: 543. https://doi.org/10.3390/e22050543

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop