A Novel Tree-Based Combined Test for Seasonality by Karsten Webel, Daniel Ollech

Subjects: “…Binary classification…”
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Using binary classification to evaluate the quality of machine translators by Ran Li, Yihao Yang, Kelin Shen, Mohammad Hijji

“…Second, the dataset is used to train the multiple binary classifiers, e.g., decision tree, random forest, extreme gradient boosting, support vector machines, logistic regression, etc. …”
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A note on limits of sequences of binary trees by Rudolf Grübel

“…For random trees the subtree size topology arises in the context of algorithms for searching and sorting when applied to random input, resulting in a sequence of nested trees. …”
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Euclidean Distance-Based Tree Algorithm for Fault Detection and Diagnosis in Photovoltaic Systems by Youssouf Mouleloued, Kamel Kara, Aissa Chouder, Abdelhadi Aouaichia, Santiago Silvestre

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An Enhanced Tree Ensemble for Classification in the Presence of Extreme Class Imbalance by Samir K. Safi, Sheema Gul

“…The efficacy of the proposed method is assessed using twenty benchmark problems for binary classification with moderate to extreme class imbalance, comparing it against other well-known methods such as optimal tree ensemble (OTE), SMOTE random forest (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi><mi>F</mi></mrow><mrow><mi>S</mi><mi>M</mi><mi>O</mi><mi>T</mi><mi>E</mi></mrow></msub></mrow></semantics></math></inline-formula>), oversampling random forest (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi mathvariant="normal">R</mi><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow></msub></mrow></semantics></math></inline-formula>), under-sampling random forest (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi mathvariant="normal">R</mi><mi mathvariant="normal">F</mi></mrow><mrow><mi mathvariant="normal">U</mi><mi mathvariant="normal">S</mi></mrow></msub></mrow></semantics></math></inline-formula>), k-nearest neighbor (k-NN), support vector machine (SVM), tree, and artificial neural network (ANN). …”
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Article RETRACTED due to manipulation by the authors Enhancing the Probability of Victor for Top Players via Knockout Tournament Fixture by Hegen Dadang Prayoga, Tomoliyus Tomoliyus, Ria Lumintuarso, Yudik prasetyo, Endang Rini Sukamti, Ari Tri Fitrianto, Andi Kasanrawali, Ramadhan Arifin, Ahmad Maulana, Muhammad Habibie

“…Keywords: Single Elimination, Binary Tree, Dummy Players, Election Procedures, Hierarchically. …”
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Training multi-layer binary neural networks with random local binary error signals by Luca Colombo, Fabrizio Pittorino, Manuel Roveri

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Deep hybrid architecture with stacked ensemble learning for binary classification of retinal disease by Priyadharsini C, Asnath Victy Phamila Y

“…Conclusion: This is the first work to experiment with 144 combinations to identify suitable deep architecture for binary retinal disease classification. The study recommends Xception for feature extraction ensembled with ExtraTreeClassifier, Light gradient boosting machine, Random Forest, AdaBoost classifiers, and meta-learner as Logistic Regression. …”
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Clinical Applicability of Machine Learning Models for Binary and Multi-Class Electrocardiogram Classification by Daniel Nasef, Demarcus Nasef, Kennette James Basco, Alana Singh, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich, Milan Toma

“…Convolutional neural networks, deep neural networks, and tree-based models, including Gradient Boosting Classifier and Random Forest, were trained and evaluated using standard metrics (accuracy, precision, recall, and F1 score) and learning curve convergence analysis. …”
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Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems by V. V. Ligi-Goryaev, G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, E. N. Dzhakhnaeva

“…Traditional classification algorithms, including LSVC (Support Vector Machine), GBT (Gradient Boosting Tree), and RF (Random Forest), have been chosen for this study. …”
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Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems by V. V. Ligi-Goryaev, G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, V. V. Dzhakhnaev

“…Traditional classification algorithms, including LSVC (Support Vector Machine), GBT (Gradient Boosting Tree), and RF (Random Forest), have been chosen for this study. …”
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Tree-Based Algorithms and Incremental Feature Optimization for Fault Detection and Diagnosis in Photovoltaic Systems by Khaled Chahine

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Upgrading Frequency Test for Overlapping Vectors and Fill Tree Tests by Krzysztof Mańk

“…This paper we analyze three tests. Starting with a range of observations made for a well-known frequency test for overlapping vectors in binary sequence testing, for which we have obtained precise chi-square statistic computed in O dt 2dt instead of O 22dt time, without precomputed tables. …”
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Person identification using novel local triangular binary pattern-based texture descriptor by Arti Tekade, T. Vijayan, B. Karthik, Anurag Mahajan

“…The performance of the proposed descriptor is assessed using different machine learning classifiers such as K-nearest neighbor (KNN), support vector machine (SVM), radial basis function-SVM (RBF-SVM), classification tree (CT), and random forest (RF) for authentication of the 20 users based on hand radiographs. …”
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Depth first traversal algorithm for the back-off tree of distributed queuing by Wennai WANG, Yanhe ZHANG, Wei WU, Chen BAI, Bin WANG

“…An analytic model was provided for the conventional distributed queueing (DQ) and its back-off tree operations, followed by a design of improving algorithm based on depth first traversal.Combing the specific analysis of complete binary tree with generalized extension by random tree reconstruction, the performance of proposed algorithm was evaluated on the throughput in both theory and simulation experiment.A theoretic optimal solution of contention slots of DQ frame and a brief description of simulation extension based on the open source NS-3 were presented.The simulation results show that the maximum stationary throughput by the proposed algorithm reaches 70% of the physical capacity of channel.…”
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IT Diagnostics of Parkinson’s Disease Based on the Analysis of Voice Markers and Machine Learning by U. A. Vishniakou, Xia YiWei

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Balanced Multi-Class Network Intrusion Detection Using Machine Learning by Faraz Ahmad Khan, Asghar Ali Shah, Nizal Alshammry, Saifullah Saif, Wasim Khan, Muhammad Osama Malik, Zahid Ullah

“…For binary classification, the Decision Tree (DT) model has exhibited the highest test accuracy of 99.96%, followed by Random Forest (99.84%), Adaboost (99.77%), and Xgboost (99.57), with the highest average precision of 100% and ROC-AUC of 99.96%. …”
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Stability of Characteristics of Physical Random Number Generators by M. O. Pikuza, S. Yu. Mikhnevich, A. Yu. Siankevich

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Performance Comparison of Random Forest and Decision Tree Algorithms for Anomaly Detection in Networks by Rafiq Fajar Ramadhan, Wahid Miftahul Ashari

“…From the study result, it can be conclude that the Decision Tree algorithm performs better in detecting anomalies in binary data with an accuracy of 99,71%. …”
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Random Generalized Additive Logistic Forest: A Novel Ensemble Method for Robust Binary Classification by Oyebayo Ridwan Olaniran, Ali Rashash R. Alzahrani, Nada MohammedSaeed Alharbi, Asma Ahmad Alzahrani

“…We introduce a novel ensemble approach, the Random Generalized Additive Logistic Forest (RGALF), which integrates generalized additive models (GAMs) within a random forest framework to improve binary classification tasks. …”
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