Sensitivity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Specificity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Fitness values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Number of features selected for IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Classification accuracy values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

F-score values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

Numerical example for determining the optimal number of clusters in Algorithm 2. by Yi Wei Tye (22208735)

Binary contingency table. by Lu Xiao (314030)

Features selected by optimization algorithms. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

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

Subjects:

RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values . by Jakub Stoklosa (3315042)

Confusion matrix for a binary outcome (adapted from Fawcett [30]). by João Albuquerque (12936572)

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

Flow diagram of the proposed model. by Uğur Ejder (22683228)

Subjects:

Convergence curve of feature selection algorithms across 11 datasets with KNN classifier. by Yi Wei Tye (22208735)

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

Process fault of Tennessee Eastman process. by Faizan e Mustafa (18004325)

“…This algorithm possesses the capability to adapt its search behavior in response to the changing dynamics of the optimization process. …”

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

“…In this study, the effects of CI and data scarcity (DS) on the performance of binary classification models were investigated using ToxCast bioassay data. …”

The comparison of the accuracy score of the benchmark and the proposed models. by Uğur Ejder (22683228)

Subjects: