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Feature selection is a fundamental step in machine learning, serving to reduce dataset redundancy, accelerate training speed, and improve model quality. This is particularly crucial in high-dimensional datasets, where the excess of features ...

• Hybrid method optimizes feature selection with multi-objective genetic algorithm.
• Achieves improved classification metrics with optimized and smaller feature sets.
• Combines multiple feature selection mechanisms for superior ...

• Solving a high-dimensional parametric engineering problem for inviscid airfoil flow.
• Simultaneous solutions across a broad spectrum of flow conditions and shapes.
• Introducing pretraining-finetuning for rapid task-specific accuracy ...

Engineering problems often involve solving partial differential equations (PDEs) over a range of similar problem setups, leading to high computational costs when using traditional numerical approaches to solve each setup individually. Recently ...

Feature selection is a crucial data preprocessing technique extensively employed in machine learning and image processing. However, feature selection encounters significant challenges when addressing high-dimensional data due to the huge and ...

• HMOFA is proposed to solve high-dimensional feature selection tasks.
• A clustering initialization method is introduced to reduce redundant features and improve the quality of initial population.
• The population updates its position ...

Feature selection in small-sample high-dimensional datasets enhances classification accuracy and reduces computational time for model training. This paper introduces the filter Classification Error Impurity (CEI) as a frequency-based ranker that ...

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• A Classification Error Impurity (CEI) algorithm is proposed as a frequency-based filter ranker.
• An Adaptive Genetic Algorithm with an External Repository (AGAwER) is proposed as a wrapper method to augment the exploration of GA.
• It ...

When dealing with large-scale datasets, high-dimensional feature selection plays a crucial role in improving the performance and interpretability of machine learning models. However, it still faces the problems of the “dimensionality curse” and ...

Spike and slab priors have emerged as effective and computationally scalable tools for Bayesian variable selection in high-dimensional linear regression. However, the crucial model selection consistency and efficient computational strategies ...

Bayesian Optimization (BO) is a popular approach to optimizing expensive-to-evaluate black-box functions. Despite the success of BO, its performance may decrease exponentially as the dimensionality increases. A common framework to tackle this ...

Feature selection in high-dimensional data is a large-scale sparse and discrete optimization problem. Most evolutionary algorithms are designed to tackle continuous optimization problems. However, when dealing with high-dimensional feature ...

• A roulette wheel-based level learning evolutionary algorithm is introduced for the high-dimensional feature selection.
• The population is divided into levels, with individuals at the higher levels guiding the lower levels for updating.

This paper presents a physics and data co-driven surrogate modeling method for efficient rare event simulation of civil and mechanical systems with high-dimensional input uncertainties. The method fuses interpretable low-fidelity physical models ...

• Physics-data-driven surrogate modeling method for estimating small probabilities.
• Fusion of physical model, error correction, active learning, and importance sampling.
• Highly efficient for rare event simulation with high-...

Parameter selection in high-dimensional models is typically fine-tuned in a way that keeps the (relative) number of false positives under control. This is because otherwise the few true positives may be dominated by the many possible false ...

In this work, we introduce the R package fabisearch, available on the Comprehensive R Archive Network (CRAN), which implements an original change point detection method for multivariate high-dimensional time series data and a new interactive, 3-...

Regression analysis is often affected by high dimensionality, severe multicollinearity, and a large proportion of missing data. These problems may mask important relationships and even lead to biased conclusions. This paper proposes a novel ...

• Proposed MultiAIS-hrlasso, an efficient variable selection algorithm for high-dimensional incomplete data.
• MultiAIS offered better consistency under challenging missing patterns.
• The hrlasso showed accuracy in variable selection ...

This paper considers the sparse estimation problem of regression coefficients in the linear model. Note that the global–local shrinkage priors do not allow the regression coefficients to be truly estimated as zero, we propose three threshold rules ...

Variable selection is a procedure to obtain truly important predictors from inputs. Complex nonlinear dependencies and strong coupling pose great challenges for variable selection in high-dimensional data. Real-world applications have increased ...

• Proposing transparent information decoupling to group inputs into four subsets.
• Adopting a nonlinear partial correlation to identify nonmonotonic relevance.
• Proposing a model-free variable selection framework for high-dimensional ...

The convexity of a set can be generalized to the two weaker notions of positive reach and r-convexity; both describe the regularity of a set’s boundary. For any compact subset of ${\mathbb{R}}^d$, we provide methods for computing upper bounds on these ...$$$\mathrm{}$$$

The concepts of sparsity, and regularised estimation, have proven useful in many high-dimensional statistical applications. Dynamic factor models (DFMs) provide a parsimonious approach to modelling high-dimensional time series, however, it is ...

A new banded time-varying precision matrix estimator is proposed for high-dimensional time series. The estimator utilizes the modified Cholesky decomposition, and the two factors in the decomposition are dynamically estimated by applying the ...

• A novel method for modeling high-dimensional time-varying banded precision matrices.
• The method ensure both Cholesky factor and innovation variance dynamic over time.
• Both the SCAD and group lasso penalty are used to obtain the ...

A binary classification problem in the high-dimensional settings is studied via the ensemble learning with each base classifier constructed from the linear discriminant analysis (LDA), and these base classifiers are integrated by the ...

Variable selection and dimension reduction have been considered in nonparametric regression for improving the precision of estimation, via the formulation of a semiparametric multiple index model. However, most existing methods are ill-equipped to ...

Deep neural network (DNN) techniques have demonstrated significant advantages over regression and some other techniques. In recent studies, DNN-based analysis has been conducted on data with high-dimensional input such as omics ...

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