Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

Relative predictive performance when using default algorithm hyperparameters and all features vs. tuning hyperparameters or selecting features. by Stephen R. Piccolo (8367780)

A workflow based on Sentinel-1 SAR data and open-source algorithms for unsupervised burned area detection in Mediterranean ecosystems by Giandomenico De Luca (11007332)

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. …”

Relative predictive performance when training on gene-expression predictors alone vs. using clinical predictors alone or gene-expression predictors in combination with clinical pre... by Stephen R. Piccolo (8367780)

Overview of analysis scenarios. by Stephen R. Piccolo (8367780)

Top 5 correlated features based on reuse. by Matthew Yit Hang Yeow (20721206)

Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species by Patricia A. Vignaux (6911525)

“…We have exhaustively curated the public data for AChE inhibition data and developed machine learning classification models for seven different species. Each set of models were built using up to nine different algorithms for each species and Morgan fingerprints (ECFP6) with an activity cutoff of 1 μM. …”

Image 1_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.tif by Xuehui Fan (10762662)

Data Sheet 7_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 2_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 9_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.xlsx by Xuehui Fan (10762662)

Data Sheet 5_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 8_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 6_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 1_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 3_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 4_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…<br> <br>Conclusion<br><br>The study concludes that the habitat variable, used in isolation, is insufficient to create a safe and reliable mushroom toxicity classification model. The consistent accuracy of 70.28% does not represent a flaw in the SVM. algorithm, but rather the predictive performance ceiling of the feature itself, whose simplicity and class overlap limit the model's discriminatory ability. …”

Systematically Scrutinizing the Impact of Substitution Sites on Thermostability and Detergent Tolerance for <i>Bacillus subtilis</i> Lipase A by Christina Nutschel (4468498)

“…Our results provide systematic and unbiased reference data at unprecedented scale for a biotechnologically important protein, identify consistently defined hot spot types for evaluating the performance of data-driven protein-engineering approaches, and show that the rigidity theory and ensemble-based approach Constraint Network Analysis yields hot spot predictions with an up to ninefold gain in precision over random classification.…”