One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

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. …”

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. …”

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. …”

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)

Results of Holm’s step-down procedure. by Nebojsa Bacanin (13932723)

MSE for ILSTM algorithm in binary classification. by Asmaa Ahmed Awad (16726315)

“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”

Flowchart depicting the structure of the BDMO algorithm. by Olatunji A. Akinola (13919696)

Results of machine learning algorithm. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset. by Nebojsa Bacanin (13932723)

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. …”

ROC comparison of machine learning algorithm. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

“…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …”

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. …”

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. …”