Comparison of ranks for classification algorithms across performance metrics. by Stephen R. Piccolo (8367780)

Top 50 genes according to average rank across feature-selection algorithms for GSE10320 and GSE46691. by Stephen R. Piccolo (8367780)

Gene-set overlap results for top 50 genes according to average rank across feature-selection algorithms for GSE46691. by Stephen R. Piccolo (8367780)

Gene-set overlap results for top 50 genes according to average rank across feature-selection algorithms for GSE10320. by Stephen R. Piccolo (8367780)

Relative classification performance per combination of feature-selection and classification algorithm. by Stephen R. Piccolo (8367780)

Relative performance of classification algorithms using gene-expression and clinical predictors and performing feature selection. by Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing feature selection. by Stephen R. Piccolo (8367780)

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML by Ayush Garg (21090944)

“…The important findings of our studies are as follows: (i) there is no effect of threshold optimization on ranking metrics such as AUC and AUPR, but AUC and AUPR get affected by class-weighting and SMOTTomek; (ii) for ML methods RF and SVM, significant percentage improvement up to 375, 33.33, and 450 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy, which are suitable for performance evaluation of imbalanced data sets; (iii) for AutoML libraries AutoGluon-Tabular and H2O AutoML, significant percentage improvement up to 383.33, 37.25, and 533.33 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy; (iv) the general pattern of percentage improvement in balanced accuracy is that the percentage improvement increases when the class ratio is systematically decreased from 0.5 to 0.1; in the case of F1 score and MCC, maximum improvement is achieved at the class ratio of 0.3; (v) for both ML and AutoML with balancing, it is observed that any individual class-balancing technique does not outperform all other methods on a significantly higher number of data sets based on F1 score; (vi) the three external balancing techniques combined outperformed the internal balancing methods of the ML and AutoML; (vii) AutoML tools perform as good as the ML models and in some cases perform even better for handling imbalanced classification when applied with imbalance handling techniques. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. Concerning National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) total score, α-RBs+ moxibustion+ auricular point sticking was most likely to be optimal treatment, the therapy ranking second was α-RBs+ needling, and the therapy ranking third was α-RBs+ moxibustion. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. Concerning National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) total score, α-RBs+ moxibustion+ auricular point sticking was most likely to be optimal treatment, the therapy ranking second was α-RBs+ needling, and the therapy ranking third was α-RBs+ moxibustion. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. Concerning National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) total score, α-RBs+ moxibustion+ auricular point sticking was most likely to be optimal treatment, the therapy ranking second was α-RBs+ needling, and the therapy ranking third was α-RBs+ moxibustion. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. Concerning National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) total score, α-RBs+ moxibustion+ auricular point sticking was most likely to be optimal treatment, the therapy ranking second was α-RBs+ needling, and the therapy ranking third was α-RBs+ moxibustion. …”

Study flowchart. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. Concerning National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) total score, α-RBs+ moxibustion+ auricular point sticking was most likely to be optimal treatment, the therapy ranking second was α-RBs+ needling, and the therapy ranking third was α-RBs+ moxibustion. …”

Risk of bias graph. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. Concerning National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) total score, α-RBs+ moxibustion+ auricular point sticking was most likely to be optimal treatment, the therapy ranking second was α-RBs+ needling, and the therapy ranking third was α-RBs+ moxibustion. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. Concerning National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) total score, α-RBs+ moxibustion+ auricular point sticking was most likely to be optimal treatment, the therapy ranking second was α-RBs+ needling, and the therapy ranking third was α-RBs+ moxibustion. …”

Characteristics of included studies. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. Concerning National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) total score, α-RBs+ moxibustion+ auricular point sticking was most likely to be optimal treatment, the therapy ranking second was α-RBs+ needling, and the therapy ranking third was α-RBs+ moxibustion. …”

Validation of image processing steps and metric results using images of a microfluidic device. by Gavrielle R. Untracht (11820311)

Flowchart of software workflow. by Gavrielle R. Untracht (11820311)

Intelligent Selection of Metal–Organic Framework Arrays for Methane Sensing via Genetic Algorithms by Jenna A. Gustafson (3744139)

“…We ran the genetic algorithm to find optimal MOF arrays of various sizes when selecting from a library of 50 different MOF materials. …”

METAHEURISTICS EVALUATION: A PROPOSAL FOR A MULTICRITERIA METHODOLOGY by Valdir Agustinho de Melo (12735562)

“…<div><p>ABSTRACT In this work we propose a multicriteria evaluation scheme for heuristic algorithms based on the classic Condorcet ranking technique. …”