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

“…<div><p>An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. …”

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

The classification algorithm flow chart for binary classification among NC, EMCI, LMCI and AD groups. by Qian Zhang (49333)

Accuracy of different algorithms in binary classification on Alibaba Cloud data set. by Zhanhui Hu (18405183)

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

Feature selection convergence time comparison of various binary metaheuristic algorithms. by Amal H. Alharbi (21755906)

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

Feature Selection for Microarray Data Classification Using Hybrid Information Gain and a Modified Binary Krill Herd Algorithm by Ge Zhang (112487)

“…A pre-screening method of feature ranking which is based on information gain (IG) and an improved binary krill herd (MBKH) algorithm are integrated in this strategy. …”

Additional file 1 of Cost-Constrained feature selection in binary classification: adaptations for greedy forward selection and genetic algorithms by Rudolf Jagdhuber (8371059)

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)

DE algorithm flow. by Ling Zhao (111365)

“…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …”

Binary classification accuracy and DR comparison on NSL-KDD. by Arshad Hashmi (13835488)

Performance of the proposed method for binary classification of lung cancer. by Md. Sabbir Hossain (9958939)

Subjects:

Information Value Scores (Binary Logistic Regression) and Feature Importance Scores (Random Forest) Across Features (for IVs from LR, a bar with dashed border indicates a significa... by Anis Ben Ghorbal (22521828)

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

Integrative Clinical and Bio-mechanical Features Predict In-Hospital Trauma Mortality by Abhishek Sawalkar (18128056)

Subjects:

Test results of different algorithms. by Ling Zhao (111365)

“…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …”

The algorithms ranking. by Moein E. Samadi (13804201)