Binary Database of Patagonian Archaeological Technology from Hunter–gatherer mobility and technological landscapes in southernmost South America: a statistical learning approach. by Ivan Briz i Godino (719098)

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)

Comparison of 14 classifiers by Jacques Wainer (570744)

Results of stratified 10-fold cross-validation runs for data set 1, 2 and 3. by Lorenz Grigull (782733)

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

“…<div><p>Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. In medical data analysis, the large number and complexity of features are often accompanied by redundant or irrelevant features, which not only increase the computational burden, but also may lead to model overfitting, which in turn affects its generalization ability. …”

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

“…<div><p>Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. In medical data analysis, the large number and complexity of features are often accompanied by redundant or irrelevant features, which not only increase the computational burden, but also may lead to model overfitting, which in turn affects its generalization ability. …”

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

“…<div><p>Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. In medical data analysis, the large number and complexity of features are often accompanied by redundant or irrelevant features, which not only increase the computational burden, but also may lead to model overfitting, which in turn affects its generalization ability. …”

LScD (Leicester Scientific Dictionary) by Neslihan Suzen (6947114)

fdata-02-00029_Reflections on Gender Analyses of Bibliographic Corpora.pdf by Helena Mihaljević (8541108)

fdata-02-00029_Reflections on Gender Analyses of Bibliographic Corpora.xml by Helena Mihaljević (8541108)

IRBMO vs. variant comparison adaptation data. by Chenyi Zhu (9383370)

“…<div><p>Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. In medical data analysis, the large number and complexity of features are often accompanied by redundant or irrelevant features, which not only increase the computational burden, but also may lead to model overfitting, which in turn affects its generalization ability. …”

Pseudo Code of RBMO. by Chenyi Zhu (9383370)

“…<div><p>Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. In medical data analysis, the large number and complexity of features are often accompanied by redundant or irrelevant features, which not only increase the computational burden, but also may lead to model overfitting, which in turn affects its generalization ability. …”

P-value on CEC-2017(Dim = 30). by Chenyi Zhu (9383370)

“…<div><p>Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. In medical data analysis, the large number and complexity of features are often accompanied by redundant or irrelevant features, which not only increase the computational burden, but also may lead to model overfitting, which in turn affects its generalization ability. …”

Memory storage behavior. by Chenyi Zhu (9383370)

“…<div><p>Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. In medical data analysis, the large number and complexity of features are often accompanied by redundant or irrelevant features, which not only increase the computational burden, but also may lead to model overfitting, which in turn affects its generalization ability. …”

Elite search behavior. by Chenyi Zhu (9383370)

“…<div><p>Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. In medical data analysis, the large number and complexity of features are often accompanied by redundant or irrelevant features, which not only increase the computational burden, but also may lead to model overfitting, which in turn affects its generalization ability. …”