Unsupervised feature selection (UFS) aims to identify a subset of the most informative features from high-dimensional data without labels. However, most existing UFS methods cannot adequately capture the intricate nonlinear relationships present ...

In high-dimensional classification, an important issue is how to enhance the performance of the classification processing mechanism. Various dimensionality reduction-based techniques such as feature selection and feature extraction have been ...

This paper presents a novel two-sample test for equal distributions in separable metric spaces, utilizing the maximum mean discrepancy (MMD). The test statistic is derived from the decomposition of the total variation of data in the reproducing ...

Aiming at the challenge posted by existing differential privacy high-dimensional data safe publishing methods, which often struggle to holistically consider the comprehensive performance of computational efficiency, privacy protection degree, and ...

The feature subset obtained by traditional feature selection algorithms usually contains many irrelevant features and redundant features, which increases the size of the feature set and reduces classification accuracy. More importantly, these ...

• The community structure is very common in large-scale BNs.
• Detect community structure in BN is vital for modeling BN by divide-and-conquer.
• The modified algorithm of A* finds the shortest paths in the BN skeleton graph.
• The ...

When constructing a Bayesian network for high-dimensional data, due to the complex relationships among distinct nodes, the difficulty in detecting the community structure will directly restrict the feasibility of the divide-and-conquer learning ...

Glioblastoma, the most malignant brain cancer in adults, exhibits vast heterogeneities in prognosis, clinicopathological features, immune landscapes, and immunotherapeutic responses, which calls the need to develop personalized therapeutic ...

In high-dimensional problems of fuzzy rule-based embedded feature selection, the challenges include loss of interpretability, curse of dimensionality, and arithmetic underflow, among others. The primary reason for these problems is the ...

Network estimation has been a critical component of high-dimensional data analysis and can provide an understanding of the underlying complex dependence structures. Among the existing studies, Gaussian graphical models have been highly popular. ...

Various fuzzy clustering-based granulation–degranulation techniques have been developed for constructing and optimizing information granules, which help reveal the underlying structure of experimental data in Granular Computing (GrC). Basically, ...

High-dimensional feature selection is of particular interest to researchers. In some domains, such as microarray data, it is quite common for a group of highly correlated explanatory variables to be of equal importance for inclusion in the ...

Feature selection (FS) is an essential pre-processing technique for high-dimensional data. Wrapper-based FS techniques are known for their superior performance over filter FS. However, when the dimensionality of data is very high the wrapper ...

The rapid development of computer and database technologies has led to the high growth of large-scale datasets. This produces an important issue for data mining applications called the curse of dimensionality, where the number of features is much ...

• A metric is proposed to measure the influence of features themselves.
• The new discriminant function is proposed to select the optimal feature subset.
• Our method can effectively reduce the dimensionality and computational ...

In high-dimensional data modeling, variable selection plays a crucial role in improving predictive accuracy and enhancing model interpretability through sparse representation. Unfortunately, certain variable selection methods encounter challenges ...

High-dimensional mislabeled learning is essential in AI theory and applications but rarely investigated. In this study, we present a novel learning technique to detect and rectify high-dimensional mislabeled data using proposed Feature Self-...

Feature selection in high-dimensional data faces significant challenges owing to large and discrete decision spaces. In this study, we propose a feature selection method based on the nondominated sorting genetic algorithm-II (NSGA-II) to enhance ...

To improve the estimation efficiency of high-dimensional regression problems, penalized regularization is routinely used. However, accurately estimating the model remains challenging, particularly in the presence of correlated effects, wherein ...${}_{\mathrm{}}$${}_{\mathrm{}}$

The determination of a tuning parameter by the generalized information criterion (GIC) is considered an important issue in variable selection. It is shown that the GIC and the L 0 penalized objective functions are equivalent, leading to a new L 0 ...

Data visualization is an essential task during the lifecycle of any Data Science (DS) project, particularly during the Exploratory Data Analysis (EDA) for a correct data preparation and understanding. In classification problems, data visualization ...

Learning algorithms can be less effective on datasets with an extensive feature space due to the presence of irrelevant and redundant features. Feature selection is a technique that effectively reduces the dimensionality of the feature space by ...