NDLI: Building predictive models in two stages with meta-learning templates optimized by genetic programming

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Building predictive models in two stages with meta-learning templates optimized by genetic programming

Content Provider	IEEE Xplore Digital Library
Author	Kordik, P. Cerny, J.
Copyright Year	2014
Abstract	The model selection stage is one of the most difficult in predictive modeling. To select a model with a highest generalization performance involves benchmarking huge number of candidate models or algorithms. Often, a final model is selected without considering potentially high quality candidates just because there are too many possibilities. Improper benchmarking methodology often leads to biased estimates of model generalization performance. Automation of the model selection stage is possible, however the computational complexity is huge especially when ensembles of models and optimization of input features should be also considered. In this paper we show, how to automate model selection process in a way that allows to search for complex hierarchies of ensemble models while maintaining computational tractability. We introduce two-stage learning, meta-learning templates optimized by evolutionary programming with anytime properties to be able to deliver and maintain data-tailored algorithms and models in a reasonable time without human interaction. Co-evolution if inputs together with optimization of templates enabled to solve algorithm selection problem efficiently for variety of datasets.
Starting Page	1
Ending Page	8
File Size	1048920
Page Count	8
File Format	PDF
ISBN	9781479945122
DOI	10.1109/CIEL.2014.7015740
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Stacking Transfer learning Hierarchical ensembles Boosting Statistics Optimization Sociology Genetic programming Meta-learning Ensembleclassifiers Cascade generalization Non-stationary environments Data models Bagging
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
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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

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