Diagnostic performance for determining malignancy; binary classification. by Seung Seog Han (4782486)

The algorithms ranking. by Moein E. Samadi (13804201)

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

“…A pre-screening method of feature ranking which is based on information gain (IG) and an improved binary krill herd (MBKH) algorithm are integrated in this strategy. …”

The maximum accuracy (lowest error rate) with the least number of ANOVA-ranked genes achieved by different feature filtering methods and classification algorithms refined by SIRRFE under various classification tasks, including: the multiclass classification for distinguishing each individual PAH patient group (Control <i>vs</i>.... by Song Cui (553627)

“…<p>The maximum accuracy (lowest error rate) with the least number of ANOVA-ranked genes achieved by different feature filtering methods and classification algorithms refined by SIRRFE under various classification tasks, including: the multiclass classification for distinguishing each individual PAH patient group (Control <i>vs</i>. …”

P-value for the Mann-Whitney U Rank Test of F1 scores (rounded to 3 decimal places) from One Category approach vs Multiple Category approach for Binary Classification Evaluation ac... by David Kilroy (10661225)

Flowchart of the proposed Margin Weighted Robust Discriminant Score (MW-RDS) algorithm. by Sheema Gul (21519289)

Friedman average rank sum test results. by Chenyi Zhu (9383370)

Random forest algorithm: Method and example results. by Natalie Weed (7345124)

“…<i>Stap2</i> rank 100, importance 0.0018.</p>…”

The SHAP (Shapley Additive exPlanations) values of observed variables. by Xin Zhang (35492)

Illustration of unprocessed-image analysis and cropped-image analysis by the algorithm. by Seung Seog Han (4782486)

Dataset selection process and exclusion criteria. by Seung Seog Han (4782486)

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. Performance evaluation, this stage is to measure the performance of the binary classification by ACC, TPR, FPR and F1 score.…”

CHIGA's impact on defect prediction performance by Sheunopa Charumbira (21011003)

“…CHIGA achieves this by combining the chi-square technique for metric ranking and a binary-encoded genetic algorithm for feature subset selection.…”

Accessibility of translation initiation sites is the strongest predictor of heterologous protein expression in <i>E. coli</i>. by Bikash K. Bhandari (11524776)

“…This partition function approach can be customised and executed using the algorithm implemented in RNAplfold. B: mRNA features ranked by Gini importance for random forest classification of the expression outcomes of the PSI:Biology targets (N = 8,780 and 2,650, ‘success’ and ‘failure’ groups, respectively). …”

Pan-cancer machine learning predictions of MEKi response. by John P. Lloyd (10196288)

“…<b>(D)</b> Performance of all combinations of models, algorithms (y-axis), and assessments by rank correlation (Spearman’s ρ, top panel) and concordance index (bottom). …”