Coordinate descent based hierarchical interactive Lasso penalized logistic regression and its application to classification problems
type: Item
instance of: scholarly article
title: Coordinate descent based hierarchical interactive Lasso penalized logistic regression and its application to classification problems
zbMATH Open document ID: 1407.62271
DOI: 10.1155/2014/430201
author: Jin-Jia Wang
author: Yang Lu
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: We present the hierarchical interactive lasso penalized logistic regression using the coordinate descent algorithm based on the hierarchy theory and variables interactions. We define the interaction model based on the geometric algebra and hierarchical constraint conditions and then use the coordinate descent algorithm to solve for the coefficients of the hierarchical interactive lasso model. We provide the results of some experiments based on UCI datasets, Madelon datasets from NIPS2003, and daily activities of the elder. The experimental results show that the variable interactions and hierarchy contribute significantly to the classification. The hierarchical interactive lasso has the advantages of the lasso and interactive lasso.
Mathematics Subject Classification ID: 62J07
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62J12
zbMATH DE Number: 7016427
Wikidata QID: Q59067124
describes a project that uses: glmnet
describes a project that uses: glinternet
describes a project that uses: LogicReg
describes a project that uses: hierNet
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/430201
OpenAlex ID: W1975417825
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description: scientific article; zbMATH DE number 7016427