Effective Dimension Reduction Research Articles

Crop yield prediction using effective deep learning and dimensionality reduction approaches for Indian regional crops

Crop yield prediction (CYP) at the field level is crucial in quantitative and economic assessment for creating agricultural commodities plans for import-export strategies and enhancing farmer incomes. Crop breeding has always required a significant amount of time and money. CYP is developed to forecast higher crop production. This paper proposes an efficient deep learning (DL) and dimensionality reduction (DR) approaches for CYP for Indian regional crops. This paper comprised ‘3′ phases: preprocessing, DR, and classification. Initially, the agricultural data of the south Indian region are collected from the dataset. Then preprocessing is applied to the collected dataset by performing data cleaning and normalization. After that, the DR is performed using squared exponential kernel-based principal component analysis (SEKPCA). Finally, CYP is based on a weight-tuned deep convolutional neural network (WTDCNN), which predicts the high crop yield profit. The simulation outcomes shows that the proposed method attains superior performance for CYP compared to exiting schemes with an improved accuracy of 98.96 %. The novelty of the proposed approach lies in the combination of DL, DR, and WTDCNN techniques for accurate crop yield prediction, specifically tailored for Indian regional crops.

Differentially private federated learning with Laplacian smoothing

Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. However, an adversary may still be able to infer the private training data by attacking the released model. Differential privacy provides a statistical protection against such attacks at the price of significantly degrading the accuracy or utility of the trained models. In this paper, we investigate a utility enhancement scheme based on Laplacian smoothing for differentially private federated learning (DP-Fed-LS), to improve the statistical precision of parameter aggregation with injected Gaussian noise without losing privacy budget. Our key observation is that the aggregated gradients in federated learning often enjoy a type of smoothness, i.e. sparsity in a graph Fourier basis with polynomial decays of Fourier coefficients as frequency grows, which can be exploited by the Laplacian smoothing efficiently. Under a prescribed differential privacy budget, convergence error bounds with tight rates are provided for DP-Fed-LS with uniform subsampling of heterogeneous non-iid data, revealing possible utility improvement of Laplacian smoothing in effective dimensionality and variance reduction, among others. Experiments over MNIST, SVHN, and Shakespeare datasets show that the proposed method can improve model accuracy with DP-guarantee and membership privacy under both uniform and Poisson subsampling mechanisms.

Reducing Effective System Dimensionality with Long-Range Collective Dipole-Dipole Interactions.

Dimensionality plays a crucial role in long-range dipole-dipole interactions (DDIs). We demonstrate that a resonant nanophotonic structure modifies the apparent dimensionality in an interacting ensemble of emitters, as revealed by population decay dynamics. Our measurements on a dense ensemble of interacting quantum emitters in a resonant nanophotonic structure with long-range DDIs reveal an effective dimensionality reduction to d[over ¯]=2.20(12), despite the emitters being distributed in 3D. This contrasts with the homogeneous environment, where the apparent dimension is d[over ¯]=3.00. Our work presents a promising avenue to manipulate dimensionality in an ensemble of interacting emitters.

Functional sufficient dimension reduction through information maximization with application to classification

Considering the case where the response variable is a categorical variable and the predictor is a random function, two novel functional sufficient dimensional reduction (FSDR) methods are proposed based on mutual information and square loss mutual information. Compared to the classical FSDR methods, such as functional sliced inverse regression and functional sliced average variance estimation, the proposed methods are appealing because they are capable of estimating multiple effective dimension reduction directions in the case of a relatively small number of categories, especially for the binary response. Moreover, the proposed methods do not require the restrictive linear conditional mean assumption and the constant covariance assumption. They avoid the inverse problem of the covariance operator which is often encountered in the functional sufficient dimension reduction. The functional principal component analysis with truncation be used as a regularization mechanism. Under some mild conditions, the statistical consistency of the proposed methods is established. Simulation studies and real data analyzes are used to evaluate the finite sample properties of our methods.

Spectrally Segmented-Enhanced Neural Network for Precise Land Cover Object Classification in Hyperspectral Imagery

The paradigm shift brought by deep learning in land cover object classification in hyperspectral images (HSIs) is undeniable, particularly in addressing the intricate 3D cube structure inherent in HSI data. Leveraging convolutional neural networks (CNNs), despite their architectural constraints, offers a promising solution for precise spectral data classification. However, challenges persist in object classification in hyperspectral imagery or hyperspectral image classification, including the curse of dimensionality, data redundancy, overfitting, and computational costs. To tackle these hurdles, we introduce the spectrally segmented-enhanced neural network (SENN), a novel model integrating segmentation-based, multi-layer CNNs, SVM classification, and spectrally segmented dimensionality reduction. SENN adeptly integrates spectral–spatial data and is particularly crucial for agricultural land classification. By strategically fusing CNNs and support vector machines (SVMs), SENN enhances class differentiation while mitigating overfitting through dropout and early stopping techniques. Our contributions extend to effective dimensionality reduction, precise CNN-based classification, and enhanced performance via CNN-SVM fusion. SENN harnesses spectral information to surmount challenges in “hyperspectral image classification in hyperspectral imagery”, marking a significant advancement in accuracy and efficiency within this domain.

Inexact augmented Lagrangian method-based full-waveform inversion with randomized singular value decomposition

Abstract The main advantage of full-waveform inversion (FWI) is the ability to obtain useful subsurface structure information, such as velocity and density, from complex seismic data. We have developed a novel inversion algorithm to improve the capability of FWI to achieve high-resolution imaging, even under complex conditions caused by random noise contamination, initial model dependence, or the selection of parameters to be estimated. Our algorithm considers an effective image processing and dimension reduction tool, randomized singular value decomposition-weighted truncated nuclear norm regularization, for embedding FWI to achieve high-resolution imaging results. This algorithm obtains a truncated matrix approximating the original matrix by reducing the rank of the velocity increment matrix, thus achieving the truncation of noisy data, with the truncation range controlled by weighted truncated nuclear norm regularization. Subsequently, we employ an inexact augmented Lagrangian method algorithm in the optimization to compress the solution space range, thus relaxing the dependence of FWI and randomized singular value decomposition-weighted truncated nuclear norm regularization on the initial model and accelerating the convergence rate of the objective function. We tested on one set of synthetic data, and the results show that, compared with traditional FWI, our method can more effectively suppress the impact of random noise, thus obtaining higher resolution and more accurate subsurface model information. This work indicates that the combination of randomized singular value decomposition-weighted truncated nuclear norm regularization and FWI is an effective imaging strategy that can help to solve the challenges faced by traditional FWI.

A feature selection method based on the Golden Jackal-Grey Wolf Hybrid Optimization Algorithm.

This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). The primary objective of this method is to create an effective data dimensionality reduction technique for eliminating redundant, irrelevant, and noisy features within high-dimensional datasets. Drawing inspiration from the Chinese idiom "Chai Lang Hu Bao," hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the GJO algorithm to propose the multi-strategy fusion GJO-GWO algorithm. In Case 1, the GJO-GWO algorithm addressed eight complex benchmark functions. In Case 2, GJO-GWO was utilized to tackle ten feature selection problems. Experimental results consistently demonstrate that under identical experimental conditions, whether solving complex benchmark functions or addressing feature selection problems, GJO-GWO exhibits smaller means, lower standard deviations, higher classification accuracy, and reduced execution times. These findings affirm the superior optimization performance, classification accuracy, and stability of the GJO-GWO algorithm.

Improved Multi-Distance ARMF Integrated With LTSA Based Pattern Matching Method and Its Application in Fault Diagnosis

Effective dimensionality reduction (DR) and classification in fault diagnosis remain a significant challenge, primarily due to the increasing scale of industrial processes and the non-linear and high-dimensional features of process data. To address this challenge, we present local tangent space alignment (LTSA) integrated with a multi-distance adaptive order morphological filter (MARMF) fault diagnosis method (LTSA-MARMF). In LTSA-MARMF, LTSA that preserves the local manifold structure using tangent space is first utilized for DR to provide the required feature space data for ARMF. Next, the cosine distance and dynamic time warping distance are introduced into the distance error of ARMF, considering the spatial similarity and dynamic features to improve classification accuracy. Finally, the distance-matching result of the pattern is applied to determine the type of fault. Through simulations, it is evident that LTSA-MARMF can achieve more satisfactory fault diagnosis accuracy than other related methods on the Tennessee-Eastman process (TEP) and the actual Grid-connected PV System (GPVS). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is inspired by the difficult-to-handle high-dimensional and non-linear features of process data but is also applicable to high-dimensional and non-linear data from other industrial processes. The DR and classification are important aspects of fault diagnosis. In this paper, a novel pattern-matching method is utilized for fault diagnosis, which uses LTSA and the modified multi-distance ARMF for DR and classification, respectively. In terms of mathematics, the distance error of ARMF is analyzed. The combination of multi-distance is used to enhance the accuracy of fault diagnosis. The preliminary experiments show that LTSA-MARMF is feasible, but has not been tested in the plant. We will consider testing LTSA-MARMF in an actual plant in future research.

MFILS: Tri-Selection via Convex and Nonconvex Regularizations.

In many real-world applications, data are represented by multiple instances and simultaneously associated with multiple labels. These data are always redundant and generally contaminated by different noise levels. As a result, several machine learning models fail to achieve good classification and find an optimal mapping. Feature selection, instance selection, and label selection are three effective dimensionality reduction techniques. Nevertheless, the literature was limited to feature and/or instance selection but has, to some extent, neglected label selection, which also plays an essential role in the preprocessing step, as label noises can adversely affect the performance of the underlying learning algorithms. In this article, we propose a novel framework termed multilabel Feature Instance Label Selection (mFILS) that simultaneously performs feature, instance, and label selections in both convex and nonconvex scenarios. To the best of our knowledge, this article offers, for the first time ever, a study using the triple and simultaneous selection of features, instances, and labels based on convex and nonconvex penalties in a multilabel scenario. Experimental results are built on some known benchmark datasets to validate the effectiveness of the proposed mFILS.

Exploring the Problems and Countermeasures in the Practice of Elderly Wisdom Sports Services in Anhui Province Based on Principal Component Analysis Methods

Abstract With the continuous progress and broad application of cutting-edge information technology such as big data, cloud computing, virtual reality and artificial intelligence, senior sports services has ushered in unprecedented development opportunities, especially the rise of senior intelligent sports services. In this study, we applied the principal component analysis method to conduct in-depth screening of evaluation indexes to assess the quality of the practice of senior intelligent sports services in Anhui Province. Through the comprehensive analysis of the eigenroots and the contribution ratio of variance, this paper successfully identified the key factors and realized the effective dimensionality reduction of the data. Meanwhile, we adopted the logistic equation to accurately estimate the relevant parameters. Through the comprehensive analysis of eigenroot and variance contribution ratio, this paper successfully identifies the key factors, realizes the effective dimensionality reduction of the data, and adopts the logistic equation to accurately estimate the relevant parameters. The analysis results show that in the senior intelligent sports service index system, the highest weight value of platform is the operation and management platform, which reaches 0.4754. In the aspect of the demand for sports information of the seniors, the demand for online sports knowledge and skills is the highest, and the demand for online sports knowledge and skills is the highest. Regarding the demand for sports information of the elderly, the online acquisition of sports knowledge and skills has the highest demand of 203, followed by the demand for regular online sports knowledge lectures of 181. In the demand for outdoor bright trails, the elderly strongly want to enter their fitness and energy consumption data for evaluation, with a high degree of demand of 351. In addition, the demand for heart rate testing of the intelligent body testing station is the highest, with a degree of 186, followed by body fat testing, with a degree of 179. The Research in this paper not only focuses on the current situation and problems of intelligent sports services for the elderly, but also explores the methods and strategies to solve these problems, aiming to provide practical reference and guidance for the effective implementation of intelligent sports services for the elderly.

Learning Accurate Label-Specific Features From Partially Multilabeled Data.

Feature selection is an effective dimensionality reduction technique, which can speed up an algorithm and improve model performance such as predictive accuracy and result comprehensibility. The study of selecting label-specific features for each class label has attracted considerable attention since each class label might be determined by some inherent characteristics, where precise label information is required to guide label-specific feature selection. However, obtaining noise-free labels is quite difficult and impractical. In reality, each instance is often annotated by a candidate label set that comprises multiple ground-truth labels and other false-positive labels, termed partial multilabel (PML) learning scenario. Here, false-positive labels concealed in a candidate label set might induce the selection of false label-specific features while masking the intrinsic label correlations, which misleads the selection of relevant features and compromises the selection performance. To address this issue, a novel two-stage partial multilabel feature selection (PMLFS) approach is proposed, which elicits credible labels to guide accurate label-specific feature selection. First, the label confidence matrix is learned to help elicit ground-truth labels from the candidate label set via the label structure reconstruction strategy, each element of which indicates how likely a class label is ground truth. After that, based on distilled credible labels, a joint selection model, including label-specific feature learner and common feature learner, is designed to learn accurate label-specific features to each class label and common features for all class labels. Besides, label correlations are fused into the features selection process to facilitate the generation of an optimal feature subset. Extensive experimental results clearly validate the superiority of the proposed approach.

Novel parameter-free and parametric same degree distribution-based dimensionality reduction algorithms for trustworthy data structure preserving

As an effective dimensionality reduction method, Same Degree Distribution (SDD) has been demonstrated to be able to maintain better data structure than other dimensionality reduction methods, including Principal Component Analysis (PCA), Multidimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Laplacian Eigen-maps (LE), Uniform Manifold Approximation and Projection (UMAP) and t-Stochastic Neighbour Embedding (t-SNE). In addition, SDD does not require tuning the number of neighbours or perplexity to scale the structure capturing performance. Instead, it requires tuning the degree of degree-distribution ranging in e certain interval. Hence, tuning the degree of degree-distribution makes SDD a less costly method than other methods that require tuning the number of neighbours or perplexity. Although these advantages, SDD is still an expensive method compared with parameter-free methods such as PCA and MDS. A parameter-free SDD is proposed based on standard SDD, with two main differences: 1) it does not require tuning the degree of degree-distribution in the entire range from 1 to 15, but only uses degree 1; and 2) it re-scales the pairwise distances in the range [0, 2] instead of range [0, 1]. A theoretical analysis is presented to prove the better performance of parameter-free SDD. In addition, the performances of the proposed parameter-free SDD and the standard SDD have been experimentally compared in terms of structure capturing and computational time. This paper also proposes a parametric version of SDD using a deep neural network approach to learn the mapping based on the samples of the original data and their corresponding embedded representations in a low dimensional space. Comparative experiments have been undertaken with SDD and other methods such as Isomap, t-SNE and UMAP to demonstrate the effectiveness of the proposed parametric SDD with several popular synthetic and real datasets such as Churn, SEER Breast Cancer, AVletters (LIPS Reading) and MNIST.

Consistency–exclusivity guided unsupervised multi-view feature selection

Unsupervised multi-view feature selection (UMFS) is an effective dimension reduction for multi-view data. It aims to obtain the important feature subset from multi-view data, which can significantly minimize the impacts of noises, outliers and redundancy. Although previous UMFS methods achieve remarkable achievements, they fail to fully take into account the consistency or the exclusivity hidden in multi-view data. To address this, this article presents a consistency–exclusivity guided unsupervised multi-view feature selection (CE-UMFS) method. Specifically, we design a multi-view matrix factorization model to simultaneously explore the consistency and exclusivity in multi-view data. Meanwhile, we employ the Hilbert–Schmidt independence criterion (HSIC) to preserve the exclusivity of different views. Furthermore, we impose a nuclear norm on the consistent representation matrix to explore the consistency across views. At last, promising experimental results demonstrate the superiority of the proposed method compared with some state-of-the-art methods.

MEHW‐SVM multi‐kernel approach for improved brain tumour classification

AbstractThe human brain, the primary constituent of the nervous system, exhibits distinctive complexities that present considerable difficulties for healthcare practitioners, specifically in categorizing brain tumours. Magnetic resonance imaging is a widely favoured imaging modality for detecting brain tumours due to its extensive range of image characteristics and utilization of non‐ionizing radiation. The primary objective of the current investigation is to differentiate between three distinct classifications of brain tumours by introducing a novel methodology. The utilization of a combined feature extraction technique that integrates novel global grey level co‐occurrence matrix and local binary patterns is employed, thereby offering a comprehensive representation of the structural and textural information contained within the images. Principal component analysis is used to improve the model's efficiency for effective feature selection and dimensionality reduction. This study presents a novel framework incorporating four separate kernel functions, Minkowski–Gaussian, exponential support vector machine (SVM), histogram intersection SVM, and wavelet kernel, into a SVM classifier. The ensemble kernel employed in this study is specifically designed to classify glioma, meningioma, and pituitary tumours. Its implementation enhances the model's robustness and adaptability, surpassing the performance of conventional single‐kernel SVM approaches. This study substantially contributes to medical image classification by utilizing innovative kernel functions and advanced machine‐learning techniques. The findings demonstrate the potential for enhanced diagnostic accuracy in brain tumour cases. The presented approach shows promise in effectively addressing the intricate challenges associated with classifying brain tumours.

A new methodology to improve wind power prediction accuracy considering power quality disturbance dimension reduction and elimination

The proper integration of wind power-driven grids relies heavily on a reliable balance between electricity production and demand. Therefore, accurate prediction is essential for planning and efficient operation of wind power systems to ensure their continuous supply. However, increasingly severe power quality disturbance (PQD) constantly disturbs this equilibrium, which affects the accuracy of wind power prediction to a large extent. For this purpose, this paper developed a novel optimization methodology to improve wind power prediction accuracy considering micro PQD dimension reduction and elimination for wind-storage integrated systems. A novel idea has been presented in this optimization methodology to eliminate the barrier of PQD and wind power prediction. In the micro aspect, a PQD dimension reduction and elimination strategy based on dynamic mode decomposition (DMD) and Wiener Filter (WF) is proposed to eliminate the autonomy of PQD. This elimination of autonomy allows the WF to get a higher signal-to-noise ratio (SNR). In the macro aspect, this paper takes PQD of different complexity into full consideration, and compares their effects on improving wind power prediction accuracy based on deep learning-based approaches. Through the experimental verification, it is confirmed that the proposed DMD-WF optimization method has demonstrated an effective dimension reduction and elimination of PQD. Moreover, it is found that the proposed PQD optimization method contributes to improve the deep learning-based prediction accuracy when PQD is more complex. The proposed methodology creates a novel perspective to improve the short-term wind power prediction accuracy, which provides a theoretical and methodological guidance for future development of large-scale integrated wind-storage systems.

Active Antinoise Fuzzy Dominance Rough Feature Selection Using Adaptive K-Nearest Neighbors

Feature selection methods with anti-noise performance are effective dimensionality reduction methods for classification tasks with noise. However, there are few studies on robust feature selection methods for monotonic classification tasks. The fuzzy dominance rough set (FDRS) model is a nontrivial knowledge acquisition tool, which is widely used in feature selection of monotonic classification tasks. Nonetheless, this model has been proved in practice to be generally poorly fault-tolerance, and only one noisy sample can cause huge interference in acquiring knowledge. In view of these two issues, this paper firstly designs an adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -nearest neighbors strategy to calculate the density of samples. The noisy samples are identified according to their densities, and then an active anti-noise fuzzy dominance rough set model is proposed. Then, in the active anti-noise fuzzy dominance rough approximation space, the class-separability is evaluated by the approximation operators of the proposed model, and the feature-redundancy is evaluated by the fuzzy ranking conditional mutual information. On this basis, a feature evaluation index is designed comprehensively considering class-separability and feature-redundancy. Finally, a feature selection algorithm is designed to select the feature subset with the highest classification performance. The experimental results show that the proposed algorithm has better robustness and classification performance.

Comparison of polynomial and rational function cornea models for effective dimensionality reduction

This study aims to examine geometric models of the corneal surface that can be used to reduce in reasonable time the dimensionality of datasets of normal anterior corneas. Polynomial models (P) like Zernike polynomials (ZP) and spherical harmonic polynomials (SHP) were obvious candidates along with their rational function (R) counterparts, namely Zernike rational functions (ZR) and spherical harmonic rational functions (SHR, new model). Knowing that both SHP and ZR were more accurate than ZP for the modeling of normal and keratoconus corneas, it was expected that both spherical harmonic (SH) models (SHP and SHR) would be more accurate than their Zernike (Z) counterparts (ZP and ZR, respectively), and both rational (R) models (SHR and ZR) more accurate than their polynomial counterparts (SHP and ZP, respectively) for a low dimensional space (coefficient number J < 30). This was the case. The SH factor contributed more to accuracy than the R factor. Considering the corneal processing time as a function of J, P models were processed in quasi-linear time with a quasi-null slope and rational models in polynomial time. Z models were faster than SH models, and increasingly so in their R version. In sum, for corneal dimensionality reduction, SHR is the most accurate model, but its processing time is increasingly prohibitive unless the best coefficient combination is identified beforehand. ZP is the fastest model and is reasonably accurate with normal corneas for exploratory tasks. SHP is the best compromise between accuracy and speed.

Joint sparse latent representation learning and dual manifold regularization for unsupervised feature selection

As an effective dimensionality reduction method, unsupervised feature selection (UFS) focuses on the mutual correlations between high-dimensional data features but often overlooks the intrinsic relationships between instances. We also utilize pseudo-labels learned from the data to guide feature selection in UFS. However, the raw data space may contain noise and outliers, leading to a lower accuracy of the learned pseudo-label matrix. We propose a minimum-redundant UFS approach to tackle these problems through jointing sparse latent representation learning with dual manifold regularization (SLRDR). Firstly, SLRDR learns a subspace of latent representation by exploring the interconnection of original data. To enhance subspace sparsity, ℓ2,1-norm is applied to the residual matrix of latent representation learning. Pseudo-label matrix learning is then carried out in the high-quality latent space, resulting in effective pseudo-label information that can provide more useful guidance for sparse regression. Secondly, based on the manifold learning hypothesis, SLRDR exploits features’ local structural properties in feature space and explores the association between data and labels, allowing the model to learn richer and more accurate structural information. In addition, ℓ2,1/2-norm is imposed on the weight matrix to obtain a minimum-redundant solution and select more discriminative features. Finally, an alternating iterative method is used for SLRDR to solve the optimization problem of the objective function, and the convergence of the model is theoretically analyzed. Besides, a series of comparative experiments with ten existing algorithms on nine benchmark datasets are used to verify the model’s effectiveness.

Reduced-order modeling with reconstruction-informed projections

Reduced-order models (ROMs) are commonly employed to address the large computational cost of simulations for high-dimensional, dynamical systems such as those found in combustion. Two key components of building these ROMs include an effective dimension reduction or projection of the high-dimensional system onto a low-dimensional manifold, alongside an accurate method for reconstructing quantities of interest (QoIs) over the projection parameters. A QoI-based approach to projection has been recently introduced, combining these aspects of building ROMs into a single training that learns the projection parameters based on nonlinear reconstruction errors of QoIs. A key advantage to this approach is the ability to inform the projection with reconstruction errors of QoIs that are functions of the projection itself, such as projected source terms influencing evolution over the low-dimensional manifold. We expand on the QoI-based projection method and implement Partition of Unity Network (POUnet) models for reconstruction, which are able to reach high levels of accuracy with small memory footprints by combining classification with localized polynomial regression. Furthermore, we find that sign changes in the projected source terms can negatively impact evolution over the low-dimensional manifold. Therefore, we also include a penalty function favoring positivity in projected source terms of the QoI-based projection method. These modeling improvements are demonstrated in one- and two-dimensional direct numerical simulations (DNS) of a non-premixed laminar flame.

SemiFREE: Semisupervised Feature Selection With Fuzzy Relevance and Redundancy

Feature selection, as an effective dimensionality reduction technique, is favored in preprocessing data. However, most existing algorithms are solely liable for labeled or unlabeled data, whereas a limited portion of real-world data is annotated with labels. In this paper, we therefore propose a novel scheme named SemiFREE, i.e., Semi-supervised feature selection with Fuzzy RElevance and rEdundancy. Firstly, both labeled and unlabeled samples are assigned with fuzzy decisions that allow class membership to naturally express the fuzziness or uncertainty in data labeling. Secondly, sample similarities in feature space and fuzzy decision are captured to induce fuzzy information measures for redefining the feature relevance and redundancy. Finally, adhering to the principle of relevance-maximization and redundancy-minimization, SemiFREE leverages the forward sequential searching strategy to identify qualified features progressively. Extensive experiments demonstrate the superiority of SemiFREE in the presence of partially labeled data against some other well-established feature selection algorithms.