A working likelihood approach to support vector regression with a data-driven insensitivity parameter

Journal article

Wu, Jinran and Wang, You-Gan. (2023). A working likelihood approach to support vector regression with a data-driven insensitivity parameter. International Journal of Machine Learning and Cybernetics. 14(3), pp. 929-945. https://doi.org/10.1007/s13042-022-01672-x
AuthorsWu, Jinran and Wang, You-Gan
Abstract

The insensitivity parameter in support vector regression determines the set of support vectors that greatly impacts the prediction. A data-driven approach is proposed to determine an approximate value for this insensitivity parameter by minimizing a generalized loss function originating from the likelihood principle. This data-driven support vector regression also statistically standardizes samples using the scale of noises different from conventional response scaling method. Statistical standardization together with probabilistic regularization based on a working likelihood function produces data-dependent values for the hyperparameters including the insensitivity parameter. The exact asymptotical solutions are provided when the noises are normally distributed. Nonlinear and linear numerical simulations with three types of noises (ϵ-Laplacian distribution, normal distribution, and uniform distribution), and in addition, five real benchmark data sets, are used to test the capacity of the proposed method. Based on all the simulations and the five case studies, the proposed support vector regression using a working likelihood, data-driven insensitivity parameter is superior and has lower computational costs.

Year2023
JournalInternational Journal of Machine Learning and Cybernetics
Journal citation14 (3), pp. 929-945
PublisherSpringer
ISSN1868-8071
Digital Object Identifier (DOI)https://doi.org/10.1007/s13042-022-01672-x
Scopus EID2-s2.0-85139614300
Open accessPublished as ‘gold’ (paid) open access
Page range929-945
Publisher's version
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File Access Level
Open
Output statusPublished
Publication dates
Online10 Oct 2022
Publication process dates
Accepted19 Sep 2022
Deposited17 Jul 2023
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Ouyang, Zhiyou, Zhai, Xu, Wu, Jinran, Yang, Jian, Yue, Dong, Dou, Chunxia and Zhang, Tengfei. (2021). A cloud endpoint coordinating CAPTCHA based on multi-view stacking ensemble. Computers & Security. 103, p. Article 102178. https://doi.org/10.1016/j.cose.2021.102178
A hybrid rolling grey framework for short time series modelling
Cui, Zhesen, Wu, Jinran, Ding, Zhe, Duan, Qibin, Lian, Wei, Yang, Yang and Cao, Taoyun. (2021). A hybrid rolling grey framework for short time series modelling. Neural Computing and Applications. 33(17), pp. 11339-11353. https://doi.org/10.1007/s00521-020-05658-0
State consensus cooperative control for a class of nonlinear multi-agent systems with output constraints via ADP approach
Yang, Yang, Fan, Xin, Xu, Chuang, Wu, Jinran and Sun, Baohua. (2021). State consensus cooperative control for a class of nonlinear multi-agent systems with output constraints via ADP approach. Neurocomputing. 458, pp. 284-296. https://doi.org/10.1016/j.neucom.2021.05.046
Small sample bias correction or bias reduction?
Zhang, Xuemao, Paul, Sudhir and Wang, You-Gan. (2021). Small sample bias correction or bias reduction? Communications in Statistics: Simulation and Computation. 50(4), pp. 1165-1177. https://doi.org/10.1080/03610918.2019.1577976
A robust and efficient variable selection method for linear regression
Yang, Zhuoran, Fu, Liya, Wang, You-Gan, Dong, Zhixiong and Jiang, Yunlu. (2021). A robust and efficient variable selection method for linear regression. Journal of Applied Statistics. 49(14), pp. 3677-3692. https://doi.org/10.1080/02664763.2021.1962259
Robust regression with asymmetric loss functions
Fu, Liya and Wang, You-Gan. (2021). Robust regression with asymmetric loss functions. Statistical Methods in Medical Research. 30(8), pp. 1800-1815. https://doi.org/10.1177/09622802211012012
A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability
Zhang, Shaotong, Wu, Ryan, Jia, Yonggang, Wang, You-Gan, Zhang, Yaqi and Duan, Qibin. (2021). A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability. Engineering Applications of Artificial Intelligence. 100, pp. 1-13. https://doi.org/10.1016/j.engappai.2021.104206
Robust approach for variable selection with high dimensional longitudinal data analysis
Fu, Liya, Li, Jiaqi and Wang, You-Gan. (2021). Robust approach for variable selection with high dimensional longitudinal data analysis. Statistics in Medicine. 40(30), pp. 6835-6854. https://doi.org/10.1002/sim.9213
Efficient and doubly-robust methods for variable selection and parameter estimation in longitudinal data analysis
Fu, Liya, Yang, Zhuoran, Cai, Fengjing and Wang, You-Gan. (2021). Efficient and doubly-robust methods for variable selection and parameter estimation in longitudinal data analysis. Computational Statistics. 36(2), pp. 781-804. https://doi.org/10.1007/s00180-020-01038-3
Predictive regression with p-lags and order-q autoregressive predictors
Jayetileke, Harshanie L., Wang, You-Gan and Zhu, Min. (2021). Predictive regression with p-lags and order-q autoregressive predictors. Journal of Empirical Finance. 62, pp. 282-293. https://doi.org/10.1016/j.jempfin.2021.04.006
An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data
Fu, Liya, Yang, Zhuoran, Zhou, Yan and Wang, You-Gan. (2021). An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data. Lifetime Data Analysis. 27(4), pp. 679-709. https://doi.org/10.1007/s10985-021-09526-4
Robust estimation procedure for autoregressive models with heterogeneity
Callens, A., Wang, Y.-G., Fu, L. and Liquet, B.. (2021). Robust estimation procedure for autoregressive models with heterogeneity. Environmental Modeling and Assessment. 26(3), pp. 313-323. https://doi.org/10.1007/s10666-020-09730-w
Influential factors on Chinese airlines’ profitability and forecasting methods
Xu, Xu, McGrory, Clare Anne, Wang, You-Gan and Wu, Jinran. (2021). Influential factors on Chinese airlines’ profitability and forecasting methods. Journal of Air Transport Management. 91, p. Article 101969. https://doi.org/10.1016/j.jairtraman.2020.101969
Support vector regression with asymmetric loss for optimal electric load forecasting
Wu, Ryan, Wang, You-Gan, Tian, Yu-Chu, Burrage, Kevin and Cao, Taoyun. (2021). Support vector regression with asymmetric loss for optimal electric load forecasting. Energy. 223, p. Article 119969. https://doi.org/10.1016/j.energy.2021.119969
Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours
Hu, Shuwen, Ingham, Aaron, Schmoelzl, Sabine, McNally, Jody, Little, Bryce, Smith, Daniel, Bishop-Hurley, Greg, Wang, You-Gan and Li, Yutao. (2020). Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours. Computers and Electronics in Agriculture. 179, p. Article 105857. https://doi.org/10.1016/j.compag.2020.105857
Response of sediments and phosphorus to catchment characteristics and human activities under different rainfall patterns with Bayesian Networks
Jin, Guangqiu, Xu, Jing, Mo, Yuming, Tang, Hongwu, Wei, Tong, Wang, You-Gan and Li, Ling. (2020). Response of sediments and phosphorus to catchment characteristics and human activities under different rainfall patterns with Bayesian Networks. Journal of Hydrology. 584, p. Article 124695. https://doi.org/10.1016/j.jhydrol.2020.124695
An improved firefly algorithm for global continuous optimization problems
Wu, Jinran, Wang, You-Gan, Burrage, Kevin, Tian, Yu-Chu, Lawson, Brodie and Ding, Zhe. (2020). An improved firefly algorithm for global continuous optimization problems. Expert Systems with Applications. 149, p. Article 113340. https://doi.org/10.1016/j.eswa.2020.113340
Bias reduction in the two-stage method for degradation data analysis
Xu, Ancha, Wang, You-Gan, Zheng, Shurong and Cai, Fengjing. (2020). Bias reduction in the two-stage method for degradation data analysis. Applied Mathematical Modelling. 77(Part 2), pp. 1413-1424. https://doi.org/10.1016/j.apm.2019.08.024
Adaptive resilient control of a class of nonlinear systems based on event-triggered mechanism
Yang, Yang, Ge, Jingzhi, Yue, Dong, Meng, Qing and Wu, Jinran. (2020). Adaptive resilient control of a class of nonlinear systems based on event-triggered mechanism. Neurocomputing. 403, pp. 304-313. https://doi.org/10.1016/j.neucom.2020.04.061
Exact algorithms for energy-efficient virtual machine placement in data centers
Wei, Chen, Hu, Zhi-Hua and Wang, You-Gan. (2020). Exact algorithms for energy-efficient virtual machine placement in data centers. Future Generation Computer Systems. 106, pp. 77-91. https://doi.org/10.1016/j.future.2019.12.043
A working likelihood approach for robust regression
Fu, Liya, Wang, You-Gan and Cai, Fengjing. (2020). A working likelihood approach for robust regression. Statistical Methods in Medical Research. 29(12), pp. 3641-3652. https://doi.org/10.1177/0962280220936310
Maritime convection and fluctuation between Vietnam and China : A data-driven study
Hu, Zhi-Hua, Liu, Chan-Juan, Chen, Wanting, Wang, You-Gan and Wei, Chen. (2020). Maritime convection and fluctuation between Vietnam and China : A data-driven study. Research in Transportation Business and Management. 34, pp. 1-15. https://doi.org/10.1016/j.rtbm.2019.100414
Identifying barley pan-genome sequence anchors using genetic mapping and machine learning
Gao, Shang, Wu, Ryan, Stiller, Jiri, Zheng, Zhi, Zhou, Meixue, Wang, You-Gan and Liu, Chunji. (2020). Identifying barley pan-genome sequence anchors using genetic mapping and machine learning. Theoretical and Applied Genetics. 133(9), pp. 2535-2544. https://doi.org/10.1007/s00122-020-03615-y
Natural mortality estimation using tree-based ensemble learning models
Liu, Chanjuan, Zhou, Shijie, Wang, You-Gan and Hu, Zhi-Hua. (2020). Natural mortality estimation using tree-based ensemble learning models. ICES Journal of Marine Science. 77(4), pp. 1414-1426. https://doi.org/10.1093/icesjms/fsaa058
Profile-guided three-phase virtual resource management for energy efficiency of data centers
Ding, Zhe, Tian, Yu-Chu, Tang, Maolin, Li, Yuefeng, Wang, You-Gan and Zhou, Chunjie. (2020). Profile-guided three-phase virtual resource management for energy efficiency of data centers. IEEE Transactions on Industrial Electronics. 67(3), pp. 2460-2468. https://doi.org/10.1109/TIE.2019.2902786
Response of water quality to land use and sewage outfalls in different seasons
Xu, Jing, Jin, Guangqiu, Tang, Hongwu, Mo, Yuming, Wang, You-Gan and Li, Ling. (2019). Response of water quality to land use and sewage outfalls in different seasons. Science of the Total Environment. 696, p. Article 134014. https://doi.org/10.1016/j.scitotenv.2019.134014
A new hybrid model to predict the electrical load in five states of Australia
Wu, Jinran, Cui, Zhesen, Chen, Yanyan, Kong, Demeng and Wang, You-Gan. (2019). A new hybrid model to predict the electrical load in five states of Australia. Energy. 166, pp. 598-609. https://doi.org/10.1016/j.energy.2018.10.076
Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite “tiny” Ne/N ratios elsewhere
Jones, Andy, Lavery, Shane D., Le Port, Agnès, Wang, You-Gan, Blower, Dean and Ovenden, Jennifer. (2019). Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite “tiny” Ne/N ratios elsewhere. Molecular Ecology. 28(12), pp. 2986-2995. https://doi.org/10.1111/mec.15130
A review of the Behrens–Fisher problem and some of its analogs : Does the same size fit all?
Paul, Sudhir, Wang, You-Gan and Ullah, Insha. (2019). A review of the Behrens–Fisher problem and some of its analogs : Does the same size fit all? Revstat Statistical Journal. 17(4), p. 563–597.
Incorporating social objectives in evaluating sustainable fisheries harvest strategy
Wu, Jiafeng, Wang, Na, Hu, Zhi-Hua, Hong, Zhenjie and Wang, You-Gan. (2019). Incorporating social objectives in evaluating sustainable fisheries harvest strategy. Environmental Modeling and Assessment. 24(4), pp. 381-386. https://doi.org/10.1007/s10666-019-9651-9
Significance tests for analyzing gene expression data with small sample sizes
Ullah, Insha, Paul, Sudhir, Hong, Zhenjie and Wang, You-Gan. (2019). Significance tests for analyzing gene expression data with small sample sizes. Bioinformatics. 35(20), pp. 3996-4003. https://doi.org/10.1093/bioinformatics/btz189
Robust Estimation Using Modified Huber’s Functions With New Tails
Jiang, Yunlu, Wang, You-Gan, Fu, Liya and Wang, Xueqin. (2019). Robust Estimation Using Modified Huber’s Functions With New Tails. Technometrics. 61(1), pp. 111-122. https://doi.org/10.1080/00401706.2018.1470037
Variable selection in rank regression for analyzing longitudinal data
Fu, Liya and Wang, You-Gan. (2018). Variable selection in rank regression for analyzing longitudinal data. Statistical Methods in Medical Research. 27(8), pp. 2447-2458. https://doi.org/10.1177/0962280216681347
Assessing temporal variations of Ammonia Nitrogen concentrations and loads in the Huaihe River Basin in relation to policies on pollution source control
Xu, Jing, Jin, Guangqiu, Tang, Hongwu, Zhang, Pei, Wang, Shen, Wang, You-Gan and Li, Ling. (2018). Assessing temporal variations of Ammonia Nitrogen concentrations and loads in the Huaihe River Basin in relation to policies on pollution source control. Science of the Total Environment. 642, pp. 1386-1395. https://doi.org/10.1016/j.scitotenv.2018.05.395
Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods
Li, Bo, Zhang, Nanxi, Wang, You-Gan, George, Andrew W., Reverter, Antonio and Li, Yutao. (2018). Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Frontiers in Genetics. 9, p. Article 237. https://doi.org/10.3389/fgene.2018.00237
Dividend growth and equity premium predictability
Zhu, Min, Chen, Rui, Du, Ke and Wang, You-Gan. (2018). Dividend growth and equity premium predictability. International Review of Economics and Finance. 56, pp. 125-137. https://doi.org/10.1016/j.iref.2017.10.020
Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations
Wang, Na, Wang, You-Gan, Hu, Shuwen, Hu, Zhi-Hua, Xu, Jing, Tang, Hongwu and Jin, Guangqiu. (2018). Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations. Environmental Modeling and Assessment. 23(6), pp. 779-786. https://doi.org/10.1007/s10666-018-9605-7
Analysis of spatial data with a nested correlation structure
Adegboye, Oyelola, Leung, Denis and Wang, You-Gan. (2018). Analysis of spatial data with a nested correlation structure. Journal of the Royal Statistical Society Series C: Applied Statistics. 67(2), pp. 329-354. https://doi.org/10.1111/rssc.12230
Working correlation structure selection in generalized estimating equations
Fu, Liya, Hao, Yangyang and Wang, You-Gan. (2018). Working correlation structure selection in generalized estimating equations. Computational Statistics. 33(2), pp. 983-996. https://doi.org/10.1007/s00180-018-0800-4
A novel hybrid model based on extreme learning machine, k-nearest neighbor regression and wavelet denoising applied to short-term electric load forecasting
Li, Weide, Kong, Demeng and Wu, Jinran. (2017). A novel hybrid model based on extreme learning machine, k-nearest neighbor regression and wavelet denoising applied to short-term electric load forecasting. Energies. 10(5), p. Article 694. https://doi.org/10.3390/en10050694
A new hybrid model FPA-SVM considering cointegration for particular matter concentration forecasting : A case study of Kunming and Yuxi, China
Li, Weide, Kong, Demeng and Wu, Jinran. (2017). A new hybrid model FPA-SVM considering cointegration for particular matter concentration forecasting : A case study of Kunming and Yuxi, China. Computational Intelligence and Neuroscience. 2017, p. Article 2843651. https://doi.org/10.1155/2017/2843651
Selection of working correlation structure in generalized estimating equations
Wang, You-Gan and Fu, Liya. (2017). Selection of working correlation structure in generalized estimating equations. Statistics in Medicine. 36(14), pp. 2206-2219. https://doi.org/10.1002/sim.7262
Blockwise AICc for model selection in generalized linear models
Song, Guofeng, Dong, Xiaogang, Wu, Jiafeng and Wang, You-Gan. (2017). Blockwise AICc for model selection in generalized linear models. Environmental Modeling and Assessment. 22(6), pp. 523-533. https://doi.org/10.1007/s10666-017-9552-8
A comment on Koh’s “The optimal design of fallible organizations : Invariance of optimal decision threshold and uniqueness of hierarchy and polyarchy structures”
Zhu, Min, Liu, Chang and Wang, You-Gan. (2017). A comment on Koh’s “The optimal design of fallible organizations : Invariance of optimal decision threshold and uniqueness of hierarchy and polyarchy structures”. Social Choice and Welfare. 48(2), pp. 385-392. https://doi.org/10.1007/s00355-016-1009-5
The Buckley–James estimator and induced smoothing
Wang, You-Gan, Zhao, Yudong and Fu, Liya. (2016). The Buckley–James estimator and induced smoothing. Australian and New Zealand Journal of Statistics. 58(2), pp. 211-225. https://doi.org/10.1111/anzs.12155
Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data
Kienzle, Marco, Sterling, David, Zhou, Shijie and Wang, You-Gan. (2016). Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data. Ecological Modelling. 322, pp. 1-9. https://doi.org/10.1016/j.ecolmodel.2015.11.008
Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment
Zischke, Mitchell T., Litherland, Lenore, Tilyard, Benjamin R., Stratford, Nicholas J., Jones, Ebony L. and Wang, You-Gan. (2016). Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment. Fisheries Research. 176, pp. 39-47. https://doi.org/10.1016/j.fishres.2015.12.003
Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data
Fu, Liya and Wang, You-Gan. (2016). Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data. Journal of Multivariate Analysis. 143, pp. 492-502. https://doi.org/10.1016/j.jmva.2015.07.004
Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data
Lloyd-Jones, Luke R., Nguyen, Hien D., McLachlan, Geoffrey J., Sumpton, Wayne and Wang, You-Gan. (2016). Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data. Biometrics. 72(4), pp. 1255-1265. https://doi.org/10.1111/biom.12531
Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas
Purcell, Steven W., Piddocke, Toby P., Dalton, Steven J. and Wang, You-Gan. (2016). Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas. Marine Ecology Progress Series. 551, pp. 201-214. https://doi.org/10.3354/meps11720
Improved confidence intervals for the linkage disequilibrium method for estimating effective population size
Jones, A. T., Ovenden, J. R. and Wang, Y.-G.. (2016). Improved confidence intervals for the linkage disequilibrium method for estimating effective population size. Heredity. 117(4), pp. 217-223. https://doi.org/10.1038/hdy.2016.19