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... by Song Cui (553627)

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. …”

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

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.…”

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

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

“…<b>Regul</b>: regularized regression; <b>RF (reg):</b> regression-based random forest; <b>Logit:</b> logistic regression; <b>RF (bin):</b> classification-based (binary) random forest.…”