Top-performing model configuration and performance by algorithm type. by Uğur Ejder (22683228)

Optimization of tracking parameters based on <i>P</i>_<i>swap</i>. by Benjamin Gallois (10131683)

Texas Rio Grande Valley Red–crowned Parrot predicted general use habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence... by Elise Varaela Voltura (17592996)

Texas Rio Grande Valley Red–crowned Parrot predicted roost site habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence)... by Elise Varaela Voltura (17592996)

Texas Rio Grande Valley Red–crowned Parrot predicted nest site habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence)... by Elise Varaela Voltura (17592996)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…<b>(E)</b> Starting from the interaction at the top of <i>I</i>_<i>δ</i>, <i>hi</i>PRS constructs <i>I</i>_<i>K</i>, selecting <i>K</i> (where <i>K</i> is user-specified) terms through the greedy optimization of the ratio between MI (<i>relevance</i>) and a suitable measure of similarity for interactions (<i>redundancy)</i> (cf. …”

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

“…EVAL1: The correlation between input features <i>x</i>∈<i>X</i> and output features y∈<i>Y</i>, <i>R</i>[<i>x,y</i>] or <i>R</i>[<i>y,x</i>]; EVAL2: The correlation between input features <i>x</i>∈<i>X</i> and labeled features v∈<i>L</i>, <i>R</i>[<i>x,v</i>] or <i>R</i>[<i>v,x</i>]; Subset: The optimal input feature subset. (D). The MCDM algorithm-Stage 4. …”

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. …”

Latent state estimations for experimental data with no stressors. by Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 18. by Samiul Alam (18563323)

Latent state estimations for experimental data with time limit. by Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 22. by Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 17. by Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 13. by Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 3. by Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 17. by Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 15. by Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 7. by Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 13. by Samiul Alam (18563323)