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. …”

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

The maximum accuracy (lowest error rate) with the least number of ANOVA-ranked genes achieved by different feature filtering methods and classification algorithms refined by SIRRFE... by Song Cui (553627)

The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. by Olaide N. Oyelade (14047002)

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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. …”

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)

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

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

Implementation of Adaptive Genetic Algorithm for classification problems by Dr.E.N. Ganesh (12315038)

“…<p>Genetic algorithms are one of the most</p> <p>commonly used approaches in data mining. …”

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)

“…In addition, hyperparameter tuning of the RF algorithm significantly improved F1 on CI assays. This study provided a basis for developing a toxicity classification model with improved performance by evaluating the effects of data set characteristics. …”

Processes in the bagged tree ensemble classification in our experiment. by Rinrada Tanthanathewin (18611171)

Explicability results of the crowding classification model. by Kengo Oka (1420585)

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The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

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

Precision-recall curve for each method using the validation predictions of the binary classification problem where the positive class is <i>malignant</i> and the negative class is... by Alberto Garcia-Zamalloa (319006)

A classification scheme of bipartite randomization algorithms, based on whether the matrix row and columns sums are preserved exactly (Fixed, <i>F</i>), preserved on average (Propo... by Zachary P. Neal (9946331)

Explicability results of the deep bite classification model. by Kengo Oka (1420585)

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Explicability results of the maxillary protrusion classification model. by Kengo Oka (1420585)

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An example binary tree for H-SuperVec(X): A hierarchical structure obtained by partitioning the class labels of each parent node into equal size subsets. by Dhananjay Kimothi (8575089)