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. …”

Binary classification task performance. by Thierry Jean (20691795)

Urgent versus nonurgent binary classification performance. by Kyung Won Kwon (18150416)

Binary contingency table. by Lu Xiao (314030)

Diagnostic performance for determining malignancy; binary classification. by Seung Seog Han (4782486)

Experimental results for the binary relevance classifier. by Antonio Fernando Lavareda Jacob Junior (17816730)

Model 1: All Variables for binary classification. by Toheeb Salahudeen (21368040)

“…RF achieved an average accuracy of 92.7% and an F1 score of 83.95% for binary classification, 90.36% and 90.1%, respectively, for the classification of three classes of severity of depression and 89.76% and 88.26%, respectively, for the classification of five classes. …”

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

Relationship between ordinal regression and binary classification performance. by Thierry Jean (20691795)

“…<p>For each of the 60 conditions (10 mental states x 2 algorithms x 3 forecast horizons), the performance of the ordinal regression model (y-axis) is displayed against the binary classification model (x-axis) under the same condition. …”

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

Subjects:

Binary classification results on the variant data (affecting/non affecting). by Yaron Strauch (12769860)

Graphical comparison of binary class lung cancer classification with different approaches. by Md. Sabbir Hossain (9958939)

Subjects:

Class distribution for binary classes. by Toheeb Salahudeen (21368040)

“…RF achieved an average accuracy of 92.7% and an F1 score of 83.95% for binary classification, 90.36% and 90.1%, respectively, for the classification of three classes of severity of depression and 89.76% and 88.26%, respectively, for the classification of five classes. …”

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

Performance metrics for random forest binary classification models. by Alex P. Lind (6945287)

Comparing the performance of One-class classifiers with their respective binary classifier. by Efrat Cohen-Davidi (19485986)

Supplementary Figure 1 from A Novel Sensitivity Maximization at a Given Specificity Method for Binary Classifications by Seyyed Mahmood Ghasemi (20815345)

Prediction AUC of Strokeformer and the competitor methods on the ten large-scale binary classification datasets. by Maocheng Cao (22127378)

Prediction AUC of Strokeformer and the competitor methods on the nine small-scale binary classification datasets. by Maocheng Cao (22127378)

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