Experimental Design for Variable Selection in Data Bases

Pumplün, Constanze; Weihs, Claus; Preußer, Andrea

doi:10.1007/3-540-28084-7_20

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…The use of experimental designs for variable selection in problems of observational data has been introduced by Pumplün et al (2005) ( [27] and [28]). Schiffner and Weihs (2009) [32] extended the simulation study of [27] in order to verify the results and as basis for further research in this field; the appropriateness of D-optimal plans for training classification methods was additionally investigated.…”

Section: Supersaturated Designsmentioning

confidence: 99%

A New Variable Selection Approach Inspired by Supersaturated Designs Given a Large-Dimensional Dataset

Parpoula¹,

Drosou²,

Koukouvinos³

et al. 2021

Journal of Data Science

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The problem of variable selection is fundamental to statistical modelling in diverse fields of sciences. In this paper, we study in particular the problem of selecting important variables in regression problems in the case where observations and labels of a real-world dataset are available. At first, we examine the performance of several existing statistical methods for analyzing a real large trauma dataset which consists of 7000 observations and 70 factors, that include demographic, transport and intrahospital data. The statistical methods employed in this work are the nonconcave penalized likelihood methods (SCAD, LASSO, and Hard), the generalized linear logistic regression, and the best subset variable selection (with AIC and BIC), used to detect possible risk factors of death. Supersaturated designs (SSDs) are a large class of factorial designs which can be used for screening out the important factors from a large set of potentially active variables. This paper presents a new variable selection approach inspired by supersaturated designs given a dataset of observations. The merits and the effectiveness of this approach for identifying important variables in observational studies are evaluated by considering several two-levels supersaturated designs, and a variety of different statistical models with respect to the combinations of factors and the number of observations. The derived results are encouraging since the alternative approach using supersaturated designs provided specific information that are logical and consistent with the medical experience, which may also assist as guidelines for trauma management.

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Section: Supersaturated Designsmentioning

confidence: 99%

A New Variable Selection Approach Inspired by Supersaturated Designs Given a Large-Dimensional Dataset

Parpoula¹,

Drosou²,

Koukouvinos³

et al. 2021

Journal of Data Science

View full text Add to dashboard Cite

show abstract

“…The experimental designs have already been used for variable selection problems given a database of observations (see, e.g., Pumplün et al . and Koukouvinos et al . ).…”

Section: Empirical Studymentioning

confidence: 99%

“…The aim of this real case study is to validate the practical use of the proposed approach and also give some insight into how the proposed screening procedure contributes toward using SSDs in real life problems. The experimental designs have already been used for variable selection problems given a database of observations (see, e.g., Pumplün et al 34 and Koukouvinos et al 35 ). Koukouvinos et al 35 dealt with a large-dimensional problem of statistical modeling given a database of observations considering an alternative approach, viz., applying database-driven cryptographic schemes for the encryption of specific fields of a database and considering SSDs.…”

Section: A Real Data Analysismentioning

confidence: 99%

A Penalized Wrapper Method for Screening Main Effects and Interactions in Supersaturated Designs

Koukouvinos

Parpoula

2014

Quality & Reliability Eng

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Supersaturated designs (SSDs) are defined as fractional factorial designs whose experimental run size is smaller than the number of main effects to be estimated. The main goal using the class of SSDs is to identify the important effects efficiently, that is, at a minimal computational cost and time. Several methods for analyzing SSDs have been proposed in recent literature. While most of the literature on SSDs has focused on main effects models, the analysis of such designs involving models with interactions has not been developed to a great extent. In this paper, we attempt to relate several penalty and loss functions with support vector machines, with one main goal in mind, screening active effects in SSDs. In this spirit, we propose a penalized wrapper screening method for identifying in one stage the important main effects and two‐factor interactions of two‐level SSDs, by assuming generalized linear models. We also carry out simulation studies and a real data analysis to assess the performance of the proposed screening procedure, showing that the proposed method works satisfactorily. Copyright © 2014 John Wiley & Sons, Ltd.

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“…Recently the experimental designs have been used for variable selection purposes. Pumplün et al (see, [24,25]) introduced the use of experimental designs for variable selection problems given a database of observations However, in that case the retrieved plan arose from the class of D-optimal designs while in our case we are interested in supersaturated designs. Schiffner and Weihs [27] extended the study of [24] and investigated the appropri-ateness of D-optimal plans for training classification methods.…”

Section: The Use Of Ssds For Variable Selectionmentioning

confidence: 99%

Supersaturated plans for variable selection in large databases

Parpoula

Koukouvinos

Simos

et al. 2014

Stat., optim. inf. comput.

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Over the last decades, the collection and storage of data has become massive with the advance of technology and variable selection has become a fundamental tool to large dimensional statistical modelling problems. In this study we implement data mining techniques, metaheuristics and use experimental designs in databases in order to determine the most relevant variables for classification in regression problems in cases where observations and labels of a large database are available. We propose a database-driven scheme for the encryption of specific fields of a database in order to select an optimal supersaturated design consisting of the variables of a large database which have been found to influence significantly the response outcome. The proposed design selection approach is quite promising, since we are able to retrieve an optimal supersaturated plan using a very small percentage of the available runs, a fact that makes the statistical analysis of a large database computationally feasible and affordable.

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Experimental Design for Variable Selection in Data Bases

Cited by 6 publications

References 4 publications

A New Variable Selection Approach Inspired by Supersaturated Designs Given a Large-Dimensional Dataset

A New Variable Selection Approach Inspired by Supersaturated Designs Given a Large-Dimensional Dataset

A Penalized Wrapper Method for Screening Main Effects and Interactions in Supersaturated Designs

Supersaturated plans for variable selection in large databases

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