Comparison between other methods in literature using KDDTest+ for binary classification. by Asmaa Ahmed Awad (16726315)

LITNET-2020 data splitting approach. 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. …”

Accuracy comparison with existing approaches for Binary Classification with state of art on UNSW-NB15 and NSL-KDD. by Arshad Hashmi (13835488)

Accuracy comparison with existing approaches for binary classification with state of art on UNSW-NB15 and NSL-KDD. by Arshad Hashmi (13835488)

Comparison of accuracy of MCL-FWA-BILSTM model with existing models for binary classification on UNSW-NB15 and NSL-KDD. by Arshad Hashmi (13835488)

MCL-FWA-BILSTM binary and multiclass accuracy comparison. by Arshad Hashmi (13835488)

Comparison between LSTM, LSTM-BOA, LSTM-CBOA and ILSTM using KDDTest+ and KDDTest-21 in binary classification with average 10 runs. by Asmaa Ahmed Awad (16726315)

Table 1_A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing... by Charmayne Mary Lee Hughes (12959972)

“…</p>Conclusion<p>Machine learning algorithms show promise for the detection of current frailty status, particularly in binary classification. …”

Flowchart of the proposed Margin Weighted Robust Discriminant Score (MW-RDS) algorithm. by Sheema Gul (21519289)

Confusion matrix for a binary outcome (adapted from Fawcett [30]). by João Albuquerque (12936572)

Grad-CAM heatmap highlighting affected regions as per the predicted class (left), and its corresponding binary activation mask using Otsu thresholding (right) to extract high-impor... by Sulav Baral (22477755)

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

“…However, ToxCast assays differ in the amount of data and degree of class imbalance (CI). Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. …”

Top-performing model configuration and performance by algorithm type. by Uğur Ejder (22683228)

Subjects:

Classification report based on HaPi. by Akib Mohi Ud Din Khanday (19065631)

The statistical description of the original data set of the patients (<i>n</i> = 162). by Uğur Ejder (22683228)

Subjects:

The list of parameters of the modified data set for machine learning (<i>n</i> = 162). by Uğur Ejder (22683228)

Subjects:

Receiver operating characteristic curve for the prostate dataset. by Hootan Kamran (21519846)

Receiver operating characteristic curve for the breast dataset. by Hootan Kamran (21519846)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…<p><b>(A)</b> Input data is a list of genotype-level SNPs. <b>(B)</b> Focusing on the positive class only, the algorithm exploits FIM (<i>apriori</i> algorithm) to build a list of candidate interactions of any desired order, retaining those that have an empirical frequency above a given threshold <i>δ</i>. …”

Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)