Ordered homogeneity pursuit lasso for group variable selection with applications to spectroscopic data

Lin, You-Wu; Xiao, Nan; Wang, Lili; Li, Chuan‐Quan; Xu, Qingsong

doi:10.1016/j.chemolab.2017.07.004

Cited by 24 publications

(11 citation statements)

References 46 publications

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“…Several solutions have been proposed in literature to address the specific problem of interval selection and in this article we described some of the main ones, namely, iPLS, 12 iVISSA, 13 iRF, 14 and OHPL 15 . We also proposed a different approach, taking advantage of variable clustering, Lasso regression, and permutation tests, called SLIS.…”

Section: Discussionmentioning

confidence: 99%

“…In this section, we are going to look at some of the main methods of interval selection found in the literature, namely, interval PLS (iPLS), 12 interval VISSA (iVISSA), 13 interval random frog (iRF), 14 and ordered homogeneity pursuit Lasso (OHPL) 15 . In addition, we are going to propose and provide a thorough description of an alternative solution, based on the usage of variable clustering, permutation tests, and Lasso regression, called SLIS.…”

Section: Competing Methodsmentioning

confidence: 99%

“…OHPL is a method introduced by Lin et al 15 to select groups of informative variables. The procedure first generates groups based on the coefficients achieved from the application of PLS to the entire dataset and then uses Lasso regression to select groups.…”

Section: Competing Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Interval selection: A case‐study‐based approach

Arboretti

Ceccato

Pegoraro

et al. 2021

Appl Stoch Models Bus & Ind

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Variable selection plays a fundamental role in the analysis of data containing several variables which are redundant or irrelevant to the problem of interest. The ability to identify and discard these variables would make it possible to improve predictive performances and data interpretation, thus reducing costs and computational time. Although many methods have been proposed for feature selection, in some fields there is more interest in selecting groups of variables because of the continuous nature and covariance of adjacent data. This is the case for near‐infrared spectroscopy, where several methods, mainly based on partial least squares regression, have been proposed to deal with interval selection. In this article, we consider some of these methods and propose an additional solution based on a variable clustering procedure (Cov/VSURF), Lasso regression and permutation tests. We compare their performances on four different public datasets and discuss the impact of interval selection on the predictive performances of the considered models.

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Section: Discussionmentioning

confidence: 99%

Section: Competing Methodsmentioning

confidence: 99%

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Interval selection: A case‐study‐based approach

Arboretti

Ceccato

Pegoraro

et al. 2021

Appl Stoch Models Bus & Ind

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“…Several methods of genes selection have been suggested to choose small and appropriate genes in high dimensional cancer classification. Some penalized likelihood methods that are capable of performing estimation of model and selection of genes efficiently have been recently considered in [8]- [10]. The most frequent and popular penalized likelihood method is the least absolute shrinkage and selection operator (LASSO) proposed in [11].…”

Section: Introductionmentioning

confidence: 99%

Improved Lasso (ILASSO) for Gene Selection and Classification in High Dimensional DNA Microarray Data

2021

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<p class="0abstract">Classification and selection of gene in high dimensional microarray data has become a challenging problem in molecular biology and genetics. Penalized Adaptive likelihood method has been employed recently for classification of cancer to address both gene selection consistency and estimation of gene coefficients in high dimensional data simultaneously. Many studies from the literature have proposed the use of ordinary least squares (OLS), maximum likelihood estimation (MLE) and Elastic net as the initial weight in the Adaptive elastic net, but in high dimensional microarray data the MLE and OLS are not suitable. Likewise, considering the Elastic net as the initial weight in Adaptive elastic yields a poor performance, because the ridge penalty in the Elastic net grouped coefficient of highly correlated genes closer to each other. As a result, the estimator fails to differentiate coefficients of highly correlated genes that have different sign being grouped together. To tackle this issue, the present study proposed Improved LASSO (ILASSO) estimator which add the ridge penalty to the original LASSO with an Adaptive weight to both and simultaneously. Results from the real data indicated that ILASSO has a better performance compared to other methods in terms of the number of gene selected, classification precision, Sensitivity and Specificity.</p>

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“…In view of the benefits of variable selection, variable selection methods based on different strategies have been proposed in large of numbers. These include the classical methods [15], [16]; the penalized methods [17], [18]; the intelligent learning algorithms [19], [20]; variable sorting strategies based on PLS model parameters [21]- [23] and variable selection methods based on the model population analysis (MPA)strategy [23]- [38] and so on. MPA as an open ensemble learning framework, in which different blocks can be filled and statistical tools to extract important information from the models.…”

Section: Introductionmentioning

confidence: 99%

An Variable Selection Method of the Significance Multivariate Correlation Competitive Population Analysis for Near-Infrared Spectroscopy in Chemical Modeling

2019

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The high dimensionality of spectral datasets makes it difficult to select the optimal subset of variables. This paper presents a new method for variable selection called the significant multivariate competitive population analysis (SMCPA), Which combines ideas of significant multivariate correlation (SMC) and model population analysis, and employs weighted bootstrap sampling (WBS) and exponential decline function (EDF) competition methods. In this study, the values of SMC distributions are used as an index for evaluating the importance of each wavelength. Then, based on the importance level of each wavelength. SMCPA sequentially selects N subsets of spectral wavelengths by N Monte Carlo sampling in an iterative and competitive procedure. In each sampling run, a fixed ratio of samples is used to build a calibrated partial least-squares model, and then SMC is performed to obtain the score and threshold values. Next, based on the significant multivariate correlation scores, the key variables are selected by two steps: the compulsory selection of exponential decline function and the competitive selection of adaptive weighted sampling. Finally, cross-validation(CV) is applied to select the optimal subset with the lowest root mean square error. This method is tested on three NIR spectral datasets and compared against three highperformance variable selection methods. The experimental results show that the proposed algorithm has the highest efficiency and the best selection effect, and can usually locate the optimal combination of key wavelength variables in a dataset. The evaluation result after PLS modeling is also the best. INDEX TERMS Spectrochemical analysis, variable selection, the significant multivariate correlation, weighted bootstrap sampling, model population analysis, monte Carlo sampling, analytical techniques, partial least squares method.

show abstract

Ordered homogeneity pursuit lasso for group variable selection with applications to spectroscopic data

Cited by 24 publications

References 46 publications

Interval selection: A case‐study‐based approach

Interval selection: A case‐study‐based approach

Improved Lasso (ILASSO) for Gene Selection and Classification in High Dimensional DNA Microarray Data

An Variable Selection Method of the Significance Multivariate Correlation Competitive Population Analysis for Near-Infrared Spectroscopy in Chemical Modeling

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