Coordinate Descent Based Hierarchical Interactive Lasso Penalized Logistic Regression and Its Application to Classification ProblemsReportar como inadecuado




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Mathematical Problems in Engineering - Volume 2014 2014, Article ID 430201, 11 pages -

Research ArticleSchool of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China

Received 20 August 2014; Revised 1 December 2014; Accepted 1 December 2014; Published 16 December 2014

Academic Editor: Wei-Chiang Hong

Copyright © 2014 Jin-Jia Wang and Yang Lu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.





Autor: Jin-Jia Wang and Yang Lu

Fuente: https://www.hindawi.com/



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