Failing Despite Knowing. Repeated Decision-Making During Binary Random Sequences with Asymmetric Probabilities by Uatkán Ajna, Ruzsa Gábor, Faragó Klára

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Evaluating the three-level approach of the U-smile method for imbalanced binary classification. by Barbara Więckowska, Katarzyna B Kubiak, Przemysław Guzik

“…Real-life binary classification problems often involve imbalanced datasets, where the majority class outnumbers the minority class. …”
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A Lower Bound on the Success Probability of Binary Random Linear Network Codes Aided by Noise Decoding by Ioannis Chatzigeorgiou

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Non-Fragile Estimation for Nonlinear Delayed Complex Networks with Random Couplings Using Binary Encoding Schemes by Nan Hou, Weijian Li, Yanhua Song, Mengdi Chang, Xianye Bu

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Analysis of motorcyclists crash severity using cluster correspondence and hierarchical binary logit models by Richard Dzinyela, Bahar Dadashova, Grant Westfall, Subasish Das, Chiara Silvestri-Dobrovolny, Emmanuel Kofi Adanu, Dominique Lord

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Understanding overfitting in random forest for probability estimation: a visualization and simulation study by Lasai Barreñada, Paula Dhiman, Dirk Timmerman, Anne-Laure Boulesteix, Ben Van Calster

“…We aimed to understand the behavior of random forests for probability estimation by (1) visualizing data space in three real-world case studies and (2) a simulation study. …”
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CREDIT CARD FRAUD DETECTION USING LINEAR DISCRIMINANT ANALYSIS (LDA), RANDOM FOREST, AND BINARY LOGISTIC REGRESSION by Muhammad Ahsan, Tabita Yuni Susanto, Tiza Ayu Virania, Andi Indra Jaya

“…In this research, we describe fraud detection as a classification issue by comparing three methods. The method used is Linear Discriminant Analysis (LDA), Random Forest, and Binary Logistic Regression. …”
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SBS Suppression Capability of Optimized Pseudo-Random Binary Sequence Phase Modulation in Multi-Stage Fiber Amplifiers by He Wang, Yifeng Yang, Kaiyuan Wang, Qianhe Shao, Xinyu Duan, Xiaolong Chen, Kai Liu, Xiaoqiang Xiong, Junqing Meng, Bing He

“…We demonstrate the capability to suppress stimulated Brillouin scattering (SBS) in a high-power all-fiber laser amplifier system using filtered and amplified pseudo-random binary sequence (PRBS) phase modulation techniques. …”
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A Multi-Kernel Mode Using a Local Binary Pattern and Random Patch Convolution for Hyperspectral Image Classification by Wei Huang, Yao Huang, Zebin Wu, Junru Yin, Qiqiang Chen

“…In order to improve classification performance while reducing costs, this article proposes a multikernel method based on a local binary pattern and random patches (LBPRP-MK), which integrates a local binary pattern (LBP) and deep learning into a multiple-kernel framework. …”
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COMPARATIVE STUDY OF SURVIVAL SUPPORT VECTOR MACHINE AND RANDOM SURVIVAL FOREST IN SURVIVAL DATA by Ni Gusti Ayu Putu Puteri Suantari, Anwar Fitrianto, Bagus Sartono

“…Random Survival Forest is tree based method that using boostrapping algorithm, and Survival Support Vector Machine using hybrid approaches between regression and ranking constrain. …”
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An effective flowchart for multimodal brain tumor binary classification with ranked 3D texture features by Mücahid Barstuğan

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A binary choice model for adoption of an emerging travel mode with unique service features by Yu Gu, Anthony Chen, Sunghoon Jang, Songyot Kitthamkesorn

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Bundled assessment to replace on-road test on driving function in stroke patients: a binary classification model via random forest by Lu Huang, Lu Huang, Xin Liu, Jiang Yi, Yu-Wei Jiao, Tian-Qi Zhang, Guang-Yao Zhu, Shu-Yue Yu, Zhong-Liang Liu, Min Gao, Xiao-Qin Duan

“…The subject was classified as either Success or Unsuccess group according to whether they had completed the on-road test. A random forest algorithm was then applied to construct a binary classification model based on the data obtained from the two groups.ResultsCompared to the Unsuccess group, the Success group had higher scores on the OCS scale for “crossing out the intact heart” (p = 0.015) and lower scores for “executive function” (p = 0.009). …”
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A Hybrid Mangrove Identification Method by Combining the Time-Frequency Threshold of the Mangrove Index With a Random Forest Binary Classifier by Zhuokai Jian, Bin Ai, Jiali Zeng, Yuchao Sun

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Wide Binaries from GAIA DR3 : testing GR vs MOND with realistic triple modelling by Charalambos Pittordis, Will Sutherland, Paul Shepherd

“…We provide an updated test for modifications of gravity from a sample of wide-binary stars from Gaia DR3, and their sky-projected relative velocities. …”
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Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS by Ahmed M. Elshewey, Enas Selem, Amira Hassan Abed

“…The performance of the proposed model is compared with another machine learning models, namely, random forest, decision tree, bagging classifier, adaptive boosting, and k-nearest neighbor, and the performance of the models is evaluated using accuracy, sensitivity, specificity, F-score, and area under the ROC curve. …”
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Formation of streams of binary sequences with controlled structure of "cellular" automata in homogeneous register environments by P. A. Kadiev, K. K. Nazarov, Z. G. Kardashova

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Controlled random tests with limited Hamming distance by V. N. Yarmolik, V. V. Petrovskaya, D. V. Demenkovets, V. A. Levantsevich

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Enhancing predictive maintenance in automotive industry: addressing class imbalance using advanced machine learning techniques by Yashashree Mahale, Shrikrishna Kolhar, Anjali S. More

“…Six machine learning models, including logistic regression, support vector machine, decision tree, and random forest, along with gradient boosting algorithms using extreme gradient boost (XGBoost) and light gradient boosting machine frameworks, were implemented. …”
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Statistical Fragility of Findings From Randomized Phase 3 Trials in Pediatric Oncology by Hannah E. Olsen, Pei‐Chi Kao, Caleb Richmond, David S. Shulman, Wendy B. London, Steven G. DuBois

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