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In feature selection, the increasing of the dimensionality and the complexity of feature interactions make the problem challenging. Furthermore, searching for an optimal subset of features from a high-dimensional feature space is known to be an $\mathcal{N}\mathcal{P}$-...

Attribute reduction is a crucial step in data pre-processing and feature engineering. It is the selection of a subset of relevant data attributes to reduce the computational complexity of machine learning models and improve their performance. ...

Unveiling the intrinsic structure within high-dimensional data presents a significant challenge, particularly when clusters manifest themselves in lower-dimensional subspaces rather than in the full feature space. This complexity is prevalent in ...

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• Novel mode-seeking algorithm for clustering high-dimensional datasets in projected subspaces.
• Subspace-determining scheme enhances accuracy of cluster identification.
• Guaranteed convergence without stopping criteria in the proposed ...

This study proposes a stacking fuzzy classifier with stochastic configuration-based learning that can achieve higher training and testing performances and sound interpretability of fuzzy rules. By using the understandable first-order Takagi–...

Feature selection depicts one of the foremost methodologies in dimensionality reduction, with its primary objective being the extraction of pertinent features from an extensive dataset. Its process is driven by two principal objectives: reducing ...

Studies on feature selection have been extensively conducted in the literature, as it plays a significant role in both supervised and unsupervised machine learning tasks. Since the bulk of features in high-dimensional data sets might not be ...

K-means clustering, which aims to distribute data points into K different clusters, is an effective technique for data mining and machine learning. However, several factors degrade clustering performance in real-world applications. High-...

In this paper, the high-dimensional linear regression problem is explored via the Elastic Net under the transfer learning framework. Within this framework, potentially related source datasets are leveraged to enhance estimation or prediction ...

Estimation of the mean and covariance parameters for functional data is a critical task, with local linear smoothing being a popular choice. In recent years, many scientific domains are producing multivariate functional data for which p, the ...

The computational cost for implementing data privacy protection tends to rise as the dimensions increase, especially on correlated datasets. For this reason, a faster data protection mechanism is needed to handle high-dimensional data while ...

• A Differential Privacy approach to handle correlated and high-dimensional data.
• Combining feature selection, hyperparameter tuning, and distributed computing.
• It reduces the computational cost in evaluating correlated ...

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 ...

• 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 ...

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 ...

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, ...