NDLI: Two Efficient Twin ELM Methods With Prediction Interval

Content Provider	IEEE Xplore Digital Library
Author	Kefeng Ning Min Liu Mingyu Dong Cheng Wu ZhanSong Wu
Copyright Year	2012
Abstract	In the operational optimization and scheduling problems of actual industrial processes, such as iron and steel, and microelectronics, the operational indices and process parameters usually need to be predicted. However, for some input and output variables of these prediction models, there may exist a lot of uncertainties coming from themselves, the measurement error, the rough representation, and so on. In such cases, constructing a prediction interval (PI) for the output of the corresponding prediction model is very necessary. In this paper, two twin extreme learning machine (TELM) models for constructing PIs are proposed. First, we propose a regularized asymmetric least squares extreme learning machine (RALS-ELM) method, in which different weights of its squared error loss function are set according to whether the error of the model output is positive or negative in order that the above error can be differentiated in the parameter learning process, and Tikhonov regularization is introduced to reduce overfitting. Then, we propose an asymmetric Bayesian extreme learning machine (AB-ELM) method based on the Bayesian framework with the asymmetric Gaussian distribution (AB-ELM), in which the weights of its likelihood function are determined as the same method in RALS-ELM, and the type II maximum likelihood algorithm is derived to learn the parameters of AB-ELM. Based on RALS-ELM and AB-ELM, we use a pair of weights following the reciprocal relationship to obtain two nonparallel regressors, including a lower-bound regressor and an upper-bound regressor, respectively, which can be used for calculating the PIs. Finally, some discussions are given, about how to adjust the weights adaptively to meet the desired PI, how to use the proposed TELMs for nonlinear quantile regression, and so on. Results of numerical comparison on data from one synthetic regression problem, three University of California Irvine benchmark regression problems, and two actual industrial regression problems show the effectiveness of the proposed models.
Page Count	14
File Size	3231586
Starting Page	2058
Ending Page	2071
File Format	PDF
ISSN	2162237X
Volume Number	26
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayes methods Gaussian distribution Steel Predictive models Temperature measurement Vectors Uncertainty twin support vector regression (TSVR). Asymmetric loss function Bayesian regression extreme learning machine (ELM) prediction interval (PI) twin support vector regression (TSVR)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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

Sparse Bayesian Extreme Learning Machine for Multi-classification

TSVR: an efficient Twin Support Vector Machine for regression.

TSVR+: Twin support vector regression with privileged information

The Connection Between Bayesian Estimation of a Gaussian Random Field and RKHS

Interval twin support vector regression algorithm for interval input-output data

GPstruct: Bayesian Structured Prediction Using Gaussian Processes

A ν-twin support vector machine based regression with automatic accuracy control

Efficient Probabilistic Classification Vector Machine With Incremental Basis Function Selection

Enhancement of parallel and non parallel twin hyperplane based learning methods using robust loss functions

Two Efficient Twin ELM Methods With Prediction Interval

Similar Documents

Sparse Bayesian Extreme Learning Machine for Multi-classification

TSVR: an efficient Twin Support Vector Machine for regression.

TSVR+: Twin support vector regression with privileged information

The Connection Between Bayesian Estimation of a Gaussian Random Field and RKHS

Interval twin support vector regression algorithm for interval input-output data

GPstruct: Bayesian Structured Prediction Using Gaussian Processes

A ν-twin support vector machine based regression with automatic accuracy control

Efficient Probabilistic Classification Vector Machine With Incremental Basis Function Selection

Enhancement of parallel and non parallel twin hyperplane based learning methods using robust loss functions

Two Efficient Twin ELM Methods With Prediction Interval