Binary contingency table. by Lu Xiao (314030)

Feature Selection for Microarray Data Classification Using Hybrid Information Gain and a Modified Binary Krill Herd Algorithm by Ge Zhang (112487)

“…However, the high dimensionality of microarray datasets poses a serious challenge to most existing FS algorithms. …”

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification by Vaibhavi Patel (19843282)

ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... by G. Christopher Marais (21492084)

Neyman-Pearson Multi-Class Classification via Cost-Sensitive Learning by Ye Tian (220278)

“…Previous studies on the NP paradigm have primarily focused on the binary case, while the multi-class NP problem poses a greater challenge due to its unknown feasibility. …”

GSE96058 information. by Sepideh Zununi Vahed (9861298)

“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”

The performance of classifiers. by Sepideh Zununi Vahed (9861298)

“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”

Data_Sheet_1_Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate Pattern Analysis.pdf by Yufen Li (428110)

“…</p><p>Methods: Based on global brain connectivity (GBC) mapping from 41 depressed ET, 49 non-depressed ET, 45 primary depression, and 43 healthy controls (HCs), multiclass Gaussian process classification (GPC) and binary support vector machine (SVM) algorithms were used to identify patients with depressed ET from non-depressed ET, primary depression, and HCs, and the accuracy and permutation tests were used to assess the classification performance.…”

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

“…<p>Overexploitation of global forest resources has led to severe survival pressure on endangered <i>Pterocarpus</i> species, and the illegal trade of their wood poses a serious threat to biodiversity and market order. …”