NDLI: Variable group selection based on regression trees: Paper machine case study

Content Provider	IEEE Xplore Digital Library
Author	Ivannikova, Elena Hamalainen, Timo Luostarinen, Kari
Copyright Year	2014
Description	Author affiliation: Department of Mathematical Information Technology, University of Jyväskylä Finland (Ivannikova, Elena; Hamalainen, Timo) \|\| Metso Paper, Jyväskylä Finland (Luostarinen, Kari)
Abstract	This paper presents a methodology for selecting best groups of predictor variables based on regression trees. Test results of the developed methodology applied to industrial pilot paper machine data are presented. Specifically, the results list process variable groups, which are more valuable in predicting paper quality variables. The benefit of paper quality prediction based on process variables is the timely reaction to changes happening during production process and, thus, the reduced operational costs. The proposed regression trees based group variable ranking methodology shows stable results on both data sets used in this study.
Starting Page	1
Ending Page	5
File Size	143430
Page Count	5
File Format	PDF
ISBN	9781479933471
DOI	10.1109/EAIS.2014.6867460
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-02
Publisher Place	Austria
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Prediction Paper quality Presses Accuracy Input variables Regression trees Pilot paper machine Data models Indexes Regression tree analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

The Study on Classification and Prediction for Data Mining

The Effects of Variable Selection Methods on Linear Regression-Based Effort Estimation Models

Boosting variable selection algorithm for linear regression models

Use of partial least squares regression for variable selection and quality prediction

Metric-based model selection for time-series forecasting

Resampling-based variable selection technique and its application to model semiconductor E-test data

Yield Modeling with Rule Ensembles

Evolving fuzzy linear regression trees with feature selection

Visualizable and interpretable regression models with good prediction power (2007)

Variable group selection based on regression trees: Paper machine case study

Similar Documents

The Study on Classification and Prediction for Data Mining

The Effects of Variable Selection Methods on Linear Regression-Based Effort Estimation Models

Boosting variable selection algorithm for linear regression models

Use of partial least squares regression for variable selection and quality prediction

Metric-based model selection for time-series forecasting

Resampling-based variable selection technique and its application to model semiconductor E-test data

Yield Modeling with Rule Ensembles

Evolving fuzzy linear regression trees with feature selection

Visualizable and interpretable regression models with good prediction power (2007)

Variable group selection based on regression trees: Paper machine case study