Binary confusion matrix. حسب Chen Zhu (445894)

Variable Selection and Estimation for Misclassified Binary Responses and Multivariate Error-Prone Predictors حسب Li-Pang Chen (9747423)

"…<p>In statistical analysis or supervised learning, classification has been an attractive topic. Typically, a main goal is to adopt predictors to characterize the primarily interested binary random variables. …"

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm حسب Hussein Ali Bardan (21976208)

"…The binary GWO algorithm identifies the most relevant features from </p><p dir="ltr">dermatological images, eliminating redundancy and reducing the computational burden. …"

Data_Sheet_1_Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM).pdf حسب Sai Sakunthala Guddanti (17739363)

"…A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing—classical, quantum, and hybrid—an attractive approach to explore. …"

Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. حسب Jiaqing Luo (10975030)

"…<p>(A). The MCDM algorithm-Stage 1. Preprocessing, this stage is the process of refining the collected raw data to eliminate noise, including correlation analysis and feature selection based on P values. …"

MCLP_quantum_annealer_V0.5 حسب Anonymous Anonymous (4854526)

"…Theoretical and applied experiments are conducted using four solvers: QBSolv, D-Wave Hybrid binary quadratic model 2, D-Wave Advantage system 4.1, and Gurobi. …"

Data_Sheet_3_sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic.docx حسب Pijush Das (3196647)

"…Many feature selection algorithms have been developed including the support vector machine recursive feature elimination procedure (SVM-RFE) and its variants. …"

Data_Sheet_2_sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic.docx حسب Pijush Das (3196647)

"…Many feature selection algorithms have been developed including the support vector machine recursive feature elimination procedure (SVM-RFE) and its variants. …"

Data_Sheet_1_sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic.docx حسب Pijush Das (3196647)

"…Many feature selection algorithms have been developed including the support vector machine recursive feature elimination procedure (SVM-RFE) and its variants. …"

UK Biobank data wrangling statistics and results. حسب Simeone Marino (81051)

Integrating terahertz time-domain spectroscopy with XGBoost for rapid and interpretable species-level wood identification of <i>Pterocarpus</i> حسب Min Yu (120607)

"…After screening the THz frequency bands and performing feature selection on THz refractive indices using the Uninformative Variable Elimination (UVE) method, the seven-class classification accuracy of the constructed UVE-XGBoost model was improved to 88.64%, confirming that the 0.1-0.3 THz band is the most important frequency range for <i>Pterocarpus</i> wood classification models. …"

DataSheet_1_Preoperatively Estimating the Malignant Potential of Mediastinal Lymph Nodes: A Pilot Study Toward Establishing a Robust Radiomics Model Based on Contrast-Enhanced CT I... حسب Mengshi Dong (5181833)

"…Multivariate logistic regression was performed with the backward stepwise elimination. A model was fitted to associate mediastinal LN malignancy with selected features. …"

DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx حسب Yuhong Huang (115702)

"…Objective<p>To investigate whether radiomics features extracted from multi-parametric MRI combining machine learning approach can predict molecular subtype and androgen receptor (AR) expression of breast cancer in a non-invasive way.…"