Boosting a Weak Learning Algorithm by Majority

Classification tree analysis using TARGET

Gray, J. B., & Fan, G. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.03.014 · Cited by 89

Slope Stability Analysis Using Rf, Gbm, Cart, Bt and Xgboost

Bharti, J. P., Mishra, P., Moorthy, U., Easwaramoorthy, S. V., Cho, Y., & Samui, P. (2021) · Geotechnical and Geological Engineering · doi:10.1007/s10706-021-01721-2 · Cited by 88

Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction

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Pattern Classification and Learning Theory

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Gradient Boosting Neural Networks: GrowNetPreprint

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Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles

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kNN: k-Nearest Neighbors

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ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches

Lei, T., Sun, H., Kang, Y., Zhu, F., Liu, H., Zhou, W., Wang, Z., Li, D., Li, Y., & Hou, T. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00631 · Cited by 90

Negative correlation learning-based RELM ensemble model integrated with OVMD for multi-step ahead wind speed forecasting

Peng, T., Zhang, C., Zhou, J., & Nazir, M. S. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.03.168 · Cited by 87

Boosting proportional hazards models using smoothing splines, with applications to high-dimensional microarray data

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Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm

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Software defect prediction using tree-based ensembles

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An overview and comprehensive comparison of ensembles for concept drift

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Learning DNF in time

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A Review of Different Components of the Intelligent Traffic Management System (ITMS)

Nigam, N., Singh, D. P., & Choudhary, J. (2023) · Symmetry · doi:10.3390/sym15030583 · Cited by 90

Learning DNF over the Uniform Distribution Using a Quantum Example Oracle

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A classification-based methodology for planning audit strategies in fraud detection

Bonchi, F., Giannotti, F., Mainetto, G., & Pedreschi, D. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312224 · Cited by 84

Object Categorization

Pinz, A. J. (2005) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000003 · Cited by 84

Statistical Learning in Palaeolimnology

Simpson, G. L., & Birks, H. J. B. (2012) · Developments in paleoenvironmental research · doi:10.1007/978-94-007-2745-8_9 · Cited by 82

Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data

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An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes

Acharya, U. R., Faust, O., Sree, S. V., Ghista, D. N., Dua, S., Joseph, P. K., Ahamed, V. I. T., Janarthanan, N., & TAMURA, T. (2011) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2011.616945 · Cited by 82

Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm

Jiang, Y., Chen, X., Yu, K., & Liao, Y. (2015) · Journal of Modern Power Systems and Clean Energy · doi:10.1007/s40565-015-0171-6 · Cited by 82

A critical evaluation of the current “p‐value controversy”

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Data Analytics for Non-Life Insurance PricingPreprint

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A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications

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Genetic ensemble of extreme learning machine

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Nonparametric pathway-based regression models for analysis of genomic data

Wei, Z., & Li, H. (2006) · Biostatistics · doi:10.1093/biostatistics/kxl007 · Cited by 81

Strong Machine Learning Attack Against PUFs with No Mathematical Model

Ganji, F., Tajik, S., Fäßler, F., & Seifert, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-53140-2_19 · Cited by 81

Leveraging the margin more carefully

Krause, N., & Singer, Y. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015344 · Cited by 80

Combination of Machine Learning and Kriging for Spatial Estimation of Geological Attributes

Erten, G. E., Yavuz, M., & Deutsch, C. V. (2022) · Natural Resources Research · doi:10.1007/s11053-021-10003-w · Cited by 85

Between Classification-Error Approximation and Weighted Least-Squares Learning

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The Case for Being Automatic: Introducing the Automatic Linear Modeling (LINEAR) Procedure in SPSS Statistics

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Autostacker

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Eye localization for face matching

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Maximum patterns in datasets

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SVM: Support Vector Machines

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On learning effective ensembles of deep neural networks for intrusion detection

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Reservoir Quality Prediction of Gas-Bearing Carbonate Sediments in the Qadirpur Field: Insights from Advanced Machine Learning Approaches of SOM and Cluster Analysis

Rashid, M., Luo, M., Ashraf, U., Hussain, W., Ali, N., Rahman, N., Hussain, S., Martyushev, D. A., Thanh, H. V., & Anees, A. (2022) · Minerals · doi:10.3390/min13010029 · Cited by 74

Research and development of neural network ensembles: a survey

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Recursive Aggregation of Estimators by the Mirror Descent Algorithm with Averaging

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PM2.5 concentration estimation using convolutional neural network and gradient boosting machine

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Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms

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Eye detection for a real-time vehicle driver fatigue monitoring system

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Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type

Hussain, M., Liu, S., Ashraf, U., Ali, M. S., Hussain, W., Ali, N., & Anees, A. (2022) · Energies · doi:10.3390/en15124501 · Cited by 70

Improving accuracy for cancer classification with a new algorithm for genes selection

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Review of statistical methods for survival analysis using genomic data

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Improving Neural-Network Classifiers Using Nearest Neighbor Partitioning

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Drought risk assessment: integrating meteorological, hydrological, agricultural and socio-economic factors using ensemble models and geospatial techniques

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Prediction of geology condition for slurry pressure balanced shield tunnel with super-large diameter by machine learning algorithms

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On Agnostic Learning of Parities, Monomials, and Halfspaces

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Three empirical studies on predicting software maintainability using ensemble methods

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FilterBoost: Regression and Classification on Large Datasets

Bradley, J. K., & Schapire, R. E. (2007) · neural information processing systems · Cited by 69

A Boosting-like Online Learning Ensemble

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Identification of Rhetorical Roles for Segmentation and Summarization of a Legal Judgment

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Behavioral authentication of server flows

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Comparison of predictive modeling approaches for 30-day all-cause non-elective readmission risk

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Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs

Lertampaiporn, S., Vorapreeda, T., Hongsthong, A., & Thammarongtham, C. (2021) · Genes · doi:10.3390/genes12020137 · Cited by 66

Viewpoint: Boosting Recessions

Ng, S. (2014) · Canadian Journal of Economics/Revue canadienne d économique · doi:10.1111/caje.12070 · Cited by 66

Selecting Instrumental Variables in a Data Rich Environment

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Machine learning models and cost-sensitive decision trees for bond rating prediction

Jabeur, S. B., Sadaaoui, A., Sghaier, A., & Aloui, R. (2019) · Journal of the Operational Research Society · doi:10.1080/01605682.2019.1581405 · Cited by 65

Active Classification based on Value of Classifier

Gao, T., & Koller, D. (2011) · Cited by 65

Penalized Regression with Ordinal Predictors

Gertheiss, J., & Tutz, G. (2009) · International Statistical Review · doi:10.1111/j.1751-5823.2009.00088.x · Cited by 66

Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost

Bin, R. D. (2016) · Computational Statistics · doi:10.1007/s00180-015-0642-2 · Cited by 64

SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images

Wu, H., Luo, W., Lin, A., Hao, F., Olteanu‐Raimond, A., Liu, L., & Li, Y. (2022) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2022.101921 · Cited by 64

Securing Electric Vehicle Performance: Machine Learning-Driven Fault Detection and Classification

Khan, M. U. I., Pathan, M. I., Rahman, M. M., Islam, M. M., Chowdhury, M. A. R., Anower, M. S., Rana, M. M., Alam, M. S., Hasan, M., Sobuj, M. S. I., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3400913 · Cited by 66

Combining Subclassifiers in Text Categorization: A DST-Based Solution and a Case Study

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Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles

Alazba, A., & Aljamaan, H. (2022) · Applied Sciences · doi:10.3390/app12094577 · Cited by 62

An ensemble deep learning model with empirical wavelet transform feature for oral cancer histopathological image classification

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Model combination for credit risk assessment: A stacked generalization approach

Doumpos, M., & Zopounidis, C. (2006) · Annals of Operations Research · doi:10.1007/s10479-006-0120-x · Cited by 61

Ten More Years of Error Rate Research

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Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded

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Searches for relativistic magnetic monopoles in IceCube

Aartsen, M. G., Abraham, K., Ackermann, M., Adams, J., Aguilar, J. A., Ahlers, M., Ahrens, M., Altmann, D., Anderson, T., Ansseau, I., et al. (2016) · The European Physical Journal C · doi:10.1140/epjc/s10052-016-3953-8 · Cited by 60

Network malware classification comparison using DPI and flow packet headers

Boukhtouta, A., Mokhov, S. A., Lakhdari, N.-E., Debbabi, M., & Paquet, J. (2015) · Journal of Computer Virology and Hacking Techniques · doi:10.1007/s11416-015-0247-x · Cited by 60

Google data in bridge equation models for German GDP

Götz, T. B., & Knetsch, T. A. (2018) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2018.08.001 · Cited by 60

Modeling highly imbalanced crash severity data by ensemble methods and global sensitivity analysis

Jiang, L., Xie, Y., Wen, X. L., & Ren, T. (2020) · Journal of Transportation Safety & Security · doi:10.1080/19439962.2020.1796863 · Cited by 60

A theory of multiclass boostingPreprint

Mukherjee, I., & Schapire, R. E. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1108.2989 · Cited by 60

Empirical Asset Pricing via Machine LearningPreprint

Gu, S., Kelly, B. T., & Xiu, D. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3281018 · Cited by 60

Stacking ensemble of machine learning methods for landslide susceptibility mapping in Zhangjiajie City, Hunan Province, China

Huan, Y., Song, L., Khan, U., & Zhang, B. (2022) · Environmental Earth Sciences · doi:10.1007/s12665-022-10723-z · Cited by 59

Feature selection algorithms for the generation of multiple classifier systems and their application to handwritten word recognition

Günter, S., & Horst Bunke, A. (2004) · Pattern Recognition Letters · doi:10.1016/j.patrec.2004.05.002 · Cited by 59

High-Dimensional Data Classification

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An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class Noise

McDonald, R. A., Hand, D. J., & Eckley, I. A. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44938-8_4 · Cited by 58

Regression Tree Analysis Using TARGET

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Performance evaluation of machine learning-based classification with rock-physics analysis of geological lithofacies in Tarakan Basin, Indonesia

Antariksa, G., Muammar, R., & Lee, J. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.109250 · Cited by 57

Evolvability from learning algorithms

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A study of the effectiveness of machine learning methods for classification of clinical interview fragments into a large number of categories

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SELECTING AMONGST LARGE CLASSES OF MODELS

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A bacterial reporter panel for the detection and classification of antibiotic substances

Melamed, S., Lalush, C., Elad, T., Yagur‐Kroll, S., Belkin, S., & Pedahzur, R. (2012) · Microbial Biotechnology · doi:10.1111/j.1751-7915.2012.00333.x · Cited by 56

Entropy Regularized LPBoost

Warmuth, M. K., Glocer, K. A., & Vishwanathan, S. V. N. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87987-9_23 · Cited by 56

Optimization by Gradient Boosting

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Improving hazard characterization in microbial risk assessment using next generation sequencing data and machine learning: Predicting clinical outcomes in shigatoxigenic Escherichia coli

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A Survey of Quantum Learning TheoryPreprint

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Compressive Sequential Learning for Action Similarity Labeling

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Every Linear Threshold Function has a Low-Weight Approximator

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Ensemble Pruning Based on Objection Maximization With a General Distributed Framework

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Simulating Hydropower Discharge using Multiple Decision Tree Methods and a Dynamical Model Merging Technique

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Deep Ensemble Machine for Video Classification

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Using machine learning for the early prediction of sepsis-associated ARDS in the ICU and identification of clinical phenotypes with differential responses to treatment

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Hierarchical distributed data classification in wireless sensor networks

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Large-Scale Prediction of Cationic Metabolite Identity and Migration Time in Capillary Electrophoresis Mass Spectrometry Using Artificial Neural Networks

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Macroeconomic forecasting using factor models and machine learning: an application to Japan

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An improved boosting algorithm and its implications on learning complexity

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The Strength of Weak Learnability

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Boosting Simple Learners

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The learning of a class of n-dimensional Boolean functions with a two input perceptron using the boosting algorithm

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Perceptron, Winnow, and PAC Learning

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