Regularization in skewed binary classification
type: Item
instance of: scholarly article
title: Regularization in skewed binary classification
zbMATH Open document ID: 0933.62050
author: Sauchi Stephen Lee
published in: Computational Statistics
publication date: 2000-03-01T00:00:00Z
review text: The paper deals with the problem of overfitting in classification of a new unknown object to one of two populations, 0 and 1, on the basis of a q-dimensional explanatory vector \(x=(x_1,\dots,x_q)\), where one of the populations, for example population 0, is the prelevent class and population 1 is a rare class. The author proposed a solution to this problem by increasing the occurrence of the rare cases by producing noisy replicates in the training data from the rare cases while keeping the objects from the dominant class without changes. He studies the effect of adding noise during training for several classification approaches: nearest neighbor method, neural networks, classification trees and quadratic discriminants. Computer experiments on three data sets from the Information and Computer Science repository of the University of California at Urvine were carried out. Promising and encouraging results are obtained.
Mathematics Subject Classification ID: 62H30
zbMATH DE Number: 1408668
zbMATH Keywords: classification
zbMATH Keywords: training data
zbMATH Keywords: noisy replicates
MaRDI profile type: Publication
full work available at URL: s001800050018
OpenAlex ID: W2086610967
cites work: Bagging predictors
cites work: Q3327527
cites work: Regularization in discriminant analysis: an overview
cites work: Q4861400
cites work: Q4314469
DOI: 10.1007/S001800050018
description: scientific article; zbMATH DE number 1408668