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)

Latin sampling. by Honghua Liu (15307022)

AUW-CE Mining Algorithms & Dataset Hub by Shuaikang Yuan (21091847)

“…Moreover, in response to the limitations of conventional cross-entropy methods for HUCPM, four core optimization strategies are designed: optimization of the initial probability distribution to guide the search direction, enhancement of sample diversity to prevent local convergence, dynamic adjustment of sample size to reduce redundant calculations, and incorporation of utility weights to improve the accuracy of probability updates. …”

NucB sampling: by Carlos A. Gomez-Uribe (21492788)

Alpha amylase sampling: by Carlos A. Gomez-Uribe (21492788)

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…<pre>Figures at Thesis_RAMIS/Figs_PI related with PhD Thesis:<br><br>AN INFORMATION-THEORETIC SAMPLING STRATEGY FOR THE RECOVERY OF GEOLOGICAL IMAGES: MODELING, ANALYSIS, AND IMPLEMENTATION<br><br>Data for the <a href="https://github.com/fsantibanezleal/FASL_Thesis_RAMIS" rel="noreferrer" target="_blank">LaTeX </a>version of the document<br><br>In this thesis the role of preferential sampling has been systematically addressed for the task of geological facies recovery using multiple-point simulation (\emph{<i>MPS</i>}) and for the problem of short-term planning in mining.  …”

PANet network design. by Mengqi Yuan (4852555)

“…Second, a dynamic up-sampling technique was introduced to improve the model’s ability to recover fine details. …”

BiFPN network design. by Mengqi Yuan (4852555)

“…Second, a dynamic up-sampling technique was introduced to improve the model’s ability to recover fine details. …”

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. …”

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization by Yingying Ma (3617810)

“…Notably, the <i>D</i> of three candidates have shown significant improvements compared to the samples with similar <i>H</i> in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. …”

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization by Yingying Ma (3617810)

“…Notably, the <i>D</i> of three candidates have shown significant improvements compared to the samples with similar <i>H</i> in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. …”

Performance of the optimal classifiers on each feature list. by Wenwei Wu (9352541)

Data_Sheet_1_Critical Evaluation of Metaheuristic Algorithms for Weight Minimization of Truss Structures.docx by Aristotelis E. Charalampakis (7454258)

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. …”