Boosting a Weak Learning Algorithm by Majority

A decision-theoretic generalization of on-line learning and an application to boosting

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A fast learning algorithm for deep belief nets

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Wrappers for feature subset selection

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Experiments with a new boosting algorithm

Freund, Y., & Schapire, R. E. (1996) · Cited by 8,189 · see citing works

Pattern Recognition and Neural Networks

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Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors

Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science · doi:10.1214/ss/1009212519 · Cited by 4,118 · see citing works

Selection of relevant features and examples in machine learning

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A Short Introduction to Boosting

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Global land cover mapping from MODIS: algorithms and early results

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Ensemble-based classifiers

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An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Bauer, E. J., & Kohavi, R. (1999) · Machine Learning · doi:10.1023/a:1007515423169 · Cited by 2,627 · see citing works

Empirical Asset Pricing via Machine Learning

Gu, S., Kelly, B., & Xiu, D. (2020) · The Review of Financial Studies · doi:10.1093/rfs/hhaa009 · Cited by 2,545 · see citing works

Boosting the margin: a new explanation for the effectiveness of voting methods

Bartlett, P. L., Freund, Y., Lee, W. S., & Schapire, R. E. (1998) · The Annals of Statistics · doi:10.1214/aos/1024691352 · Cited by 2,541 · see citing works

Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction

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The Boosting Approach to Machine Learning: An Overview

Schapire, R. E. (2003) · Lecture notes in statistics · doi:10.1007/978-0-387-21579-2_9 · Cited by 2,060 · see citing works

Ensembling neural networks: Many could be better than all

Zhou, Z.-H., Wu, J., & Tang, W. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00190-x · Cited by 1,964 · see citing works

Extreme learning machines: a survey

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A survey on object detection in optical remote sensing images

Cheng, G., & Han, J. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.03.014 · Cited by 1,503 · see citing works

A brief introduction to boosting

Schapire, R. E. (1999) · Cited by 1,301 · see citing works

Fitness Approximation In Evolutionary Computation - a Survey.

Jin, Y., & Sendhoff, B. (2002) · Surrey Research Insight Open Access (The University of Surrey) · doi:10.1007/s00500-003-0328-5 · Cited by 57 · see citing works

Explaining AdaBoost

Schapire, R. E. (2013) · Springer eBooks · doi:10.1007/978-3-642-41136-6_5 · Cited by 1,201 · see citing works

Foundations of Machine Learning

Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) · doi:10.7551/mitpress/10290.001.0001 · Cited by 1,122 · see citing works

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges

Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A., et al. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1484 · Cited by 1,118 · see citing works

Arcing classifier (with discussion and a rejoinder by the author)

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A survey of multiple classifier systems as hybrid systems

Woźniak, M., Graña, M., & Corchado, E. (2013) · Information Fusion · doi:10.1016/j.inffus.2013.04.006 · Cited by 909 · see citing works

Boosting With the L 2 Loss

Bühlmann, P., & Yu, B. (2003) · Journal of the American Statistical Association · doi:10.1198/016214503000125 · Cited by 838 · see citing works

A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

Statnikov, A., Aliferis, C., Tsamardinos, I., Hardin, D. P., & Levy, S. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti033 · Cited by 837 · see citing works

Unsupervised Models for Named Entity Classification

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Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains

Wardlow, B. D., & Egbert, S. L. (2007) · Remote Sensing of Environment · doi:10.1016/j.rse.2007.07.019 · Cited by 771 · see citing works

A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection

Mishra, P., Varadharajan, V., Tupakula, U. K., & Pilli, E. S. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2847722 · Cited by 745 · see citing works

A Review of Computer Vision Techniques for the Analysis of Urban Traffic

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Downscaling of precipitation for climate change scenarios: A support vector machine approach

Tripathi, S., Srinivas, V. V., & Nanjundiah, R. S. (2006) · Journal of Hydrology · doi:10.1016/j.jhydrol.2006.04.030 · Cited by 609 · see citing works

Classification and Regression Trees, Bagging, and Boosting

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Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

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Neural Probabilistic Language Models

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What is Machine Learning? A Primer for the Epidemiologist

Bi, Q., Goodman, K. E., Kaminsky, J., & Lessler, J. (2019) · American Journal of Epidemiology · doi:10.1093/aje/kwz189 · Cited by 522 · see citing works

Gradient boosting machine for modeling the energy consumption of commercial buildings

Touzani, S., Granderson, J., & Fernandes, S. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.11.039 · Cited by 508 · see citing works

Random Forests and Adaptive Nearest Neighbors

Lin, Y., & Jeon, Y. (2006) · Journal of the American Statistical Association · doi:10.1198/016214505000001230 · Cited by 507 · see citing works

An Introduction to Boosting and Leveraging

Meir, R., & Rätsch, G. (2003) · Lecture notes in computer science · doi:10.1007/3-540-36434-x_4 · Cited by 454 · see citing works

Empirical Margin Distributions and Bounding the Generalization Error of Combined Classifiers

Koltchinskii, V. I., & Panchenko, D. (2002) · The Annals of Statistics · doi:10.1214/aos/1015362183 · Cited by 451 · see citing works

Theory of Classification: a Survey of Some Recent Advances

Boucheron, S., Bousquet, O., & Lugosi, G. (2005) · ESAIM Probability and Statistics · doi:10.1051/ps:2005018 · Cited by 442 · see citing works

Empirical Asset Pricing via Machine Learning

Gu, S., Kelly, B., & Xiu, D. (2018) · National Bureau of Economic Research · doi:10.3386/w25398 · Cited by 410 · see citing works

An analysis of diversity measures

Tang, E. K., Suganthan, P. N., & Yao, X. (2006) · Machine Learning · doi:10.1007/s10994-006-9449-2 · Cited by 402 · see citing works

Data Mining With R: Learning By Case Studies

Torgo, L. (2010) · Cited by 398 · see citing works

Game theory, on-line prediction and boosting

Freund, Y., & Schapire, R. E. (1996) · Proceedings of the ninth annual conference on Computational learning theory - COLT '96 · doi:10.1145/238061.238163 · Cited by 383 · see citing works

Bagging, Boosting and Ensemble Methods

Bühlmann, P. (2011) · Springer eBooks · doi:10.1007/978-3-642-21551-3_33 · Cited by 374 · see citing works

Machine Learning and Deep Learning in Energy Systems: A Review

Forootan, M. M., Larki, I., Zahedi, R., & Ahmadi, A. (2022) · Sustainability · doi:10.3390/su14084832 · Cited by 373 · see citing works

High-Performance Rotation Invariant Multiview Face Detection

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Long sequence time-series forecasting with deep learning: A survey

Chen, Z., Ma, M., Li, T., Wang, H., & Li, C. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101819 · Cited by 359 · see citing works

A simple, fast, and effective rule learner

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Machine learning in construction: From shallow to deep learning

Xu, Y., Zhou, Y., Sekuła, P., & Ding, L. (2021) · Developments in the Built Environment · doi:10.1016/j.dibe.2021.100045 · Cited by 337 · see citing works

SemiBoost: Boosting for Semi-Supervised Learning

Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.235 · Cited by 335 · see citing works

Ensemble of online sequential extreme learning machine

Lan, Y., Soh, Y. C., & Huang, G.-B. (2009) · Neurocomputing · doi:10.1016/j.neucom.2009.02.013 · Cited by 333 · see citing works

Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions

Somorjai, R. L., Dolenko, B., & Baumgartner, R. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg182 · Cited by 326 · see citing works

Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models

Storlie, C. B., Swiler, L. P., Helton, J. C., & Sallaberry, C. J.-M. (2009) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2009.05.007 · Cited by 319 · see citing works

Computational learning theory

Angluin, D. (1992) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/129712.129746 · Cited by 318 · see citing works

Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar

Liu, M., Wang, M., Wang, J., & Li, D. (2012) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2012.11.071 · Cited by 318 · see citing works

Boosting Algorithms: A Review of Methods, Theory, and Applications

Ferreira, A. J., & Figueiredo, M. A. T. (2012) · Springer eBooks · doi:10.1007/978-1-4419-9326-7_2 · Cited by 313 · see citing works

Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control

Fasel, U., Kutz, J. N., Brunton, B. W., & Brunton, S. L. (2022) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2021.0904 · Cited by 306 · see citing works

A review of supervised machine learning algorithms and their applications to ecological data

Crisci, C., Ghattas, B., & Perera, G. (2012) · Ecological Modelling · doi:10.1016/j.ecolmodel.2012.03.001 · Cited by 300 · see citing works

A gradient boosting approach to the Kaggle load forecasting competition

Taieb, S. B., & Hyndman, R. J. (2013) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2013.07.005 · Cited by 296 · see citing works

Machine learning in geo- and environmental sciences: From small to large scale

Tahmasebi, P., Kamrava, S., Bai, T., & Sahimi, M. (2020) · Advances in Water Resources · doi:10.1016/j.advwatres.2020.103619 · Cited by 294 · see citing works

Boosting Neural Networks

Schwenk, H., & Bengio, Y. (2000) · Neural Computation · doi:10.1162/089976600300015178 · Cited by 293 · see citing works

Ensembles of Learning Machines

Valentini, G., & Masulli, F. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45808-5_1 · Cited by 289 · see citing works

User re-authentication via mouse movements

Pusara, M., & Brodley, C. E. (2004) · IEEE Symposium on Visualization for Cyber Security (VIZSEC) · doi:10.1145/1029208.1029210 · Cited by 287 · see citing works

An Adaptive Version of the Boost by Majority Algorithm

Freund, Y. (2001) · Machine Learning · doi:10.1023/a:1010852229904 · Cited by 282 · see citing works

Ensemble with neural networks for bankruptcy prediction

Kim, M.-J., & Kang, D.-K. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.10.012 · Cited by 270 · see citing works

The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: TutorialPreprint

Ghojogh, B., & Crowley, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12787 · Cited by 269 · see citing works

Using output codes to boost multiclass learning problems

Schapire, R. E. (1997) · Cited by 259 · see citing works

PolyNet: A Pursuit of Structural Diversity in Very Deep Networks

Zhang, X., Li, Z., Loy, C. C., & Lin, D. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.415 · Cited by 258 · see citing works

Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension

Floyd, S., & Warmuth, M. K. (1995) · Machine Learning · doi:10.1023/a:1022660318680 · Cited by 255 · see citing works

Hierarchical Modeling, Optimization, and Synthesis for System-Level Analog and RF Designs

Rutenbar, R. A., Gielen, G. E., & Roychowdhury, J. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2006.889371 · Cited by 250 · see citing works

A Cell Proliferation Signature Is a Marker of Extremely Poor Outcome in a Subpopulation of Breast Cancer Patients

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A data-driven statistical model for predicting the critical temperature of a superconductor

Hamidieh, K. (2018) · Computational Materials Science · doi:10.1016/j.commatsci.2018.07.052 · Cited by 250 · see citing works

A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection

Yasui, Y. (2003) · Biostatistics · doi:10.1093/biostatistics/4.3.449 · Cited by 249 · see citing works

Semiparametric regression during 2003–2007

Ruppert, D., WAND, M. P., & Carroll, R. J. (2009) · Electronic Journal of Statistics · doi:10.1214/09-ejs525 · Cited by 245 · see citing works

Water body extraction from Landsat ETM+ imagery using adaboost algorithm

Shen, L., & Li, C. (2010) · International Conference on Geoinformatics · doi:10.1109/geoinformatics.2010.5567762 · Cited by 244 · see citing works

Current Trends and Applications of Machine Learning in Tribology—A Review

Marian, M., & Tremmel, S. (2021) · Lubricants · doi:10.3390/lubricants9090086 · Cited by 238 · see citing works

Spectral Clustering Ensemble Applied to SAR Image Segmentation

Zhang, X., Jiao, L., Liu, F., Bo, L., & Gong, M. (2008) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.918647 · Cited by 226 · see citing works

Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset

Chris Hinrichs, A., Singh, V., Mukherjee, L., Xu, G., Chung, M. K., & Johnson, S. C. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.056 · Cited by 226 · see citing works

Cost-sensitive boosting neural networks for software defect prediction

Zheng, J. J. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.12.056 · Cited by 225 · see citing works

Optimizing ensemble weights and hyperparameters of machine learning models for regression problems

Shahhosseini, M., Hu, G., & Pham, H. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100251 · Cited by 222 · see citing works

DATA STREAM MINING A Practical Approach

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A systematic review for transformer-based long-term series forecasting

Su, L., Zuo, X., Li, R., Wang, X., Zhao, H., & Huang, B. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11044-2 · Cited by 219 · see citing works

Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature

Shi, Z., Yu, X., Jiang, Z., & Li, B. (2013) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2282355 · Cited by 213 · see citing works

Margin Analysis of the LVQ Algorithm

Crammer, K., Gilad-Bachrach, R., Navot, A., & Tishby, N. (2002) · Cited by 212 · see citing works

Using prior probabilities in decision-tree classification of remotely sensed data

McIver, D. K., & Friedl, M. A. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(02)00003-2 · Cited by 211 · see citing works

Using Correspondence Analysis to Combine Classifiers

Merz, C. J. (1999) · Machine Learning · doi:10.1023/a:1007559205422 · Cited by 209 · see citing works

PLS Dimension Reduction for Classification with Microarray Data

Boulesteix, A. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1075 · Cited by 207 · see citing works

Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree

Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.928917 · Cited by 205 · see citing works

Hyperspectral Image Classification in the Presence of Noisy Labels

Jiang, J., Ma, J., Wang, Z., Chen, C., & Liu, X. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2861992 · Cited by 205 · see citing works

Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions

Liu, H., Tian, H.-Q., Li, Y.-F., & Zhang, L. (2015) · Energy Conversion and Management · doi:10.1016/j.enconman.2014.12.053 · Cited by 198 · see citing works

Bayesian Approaches to Failure Prediction for Disk Drives

Hamerly, G., & Elkan, C. (2001) · Cited by 196 · see citing works

Fast decorrelated neural network ensembles with random weights

Alhamdoosh, M., & Wang, D. (2014) · Information Sciences · doi:10.1016/j.ins.2013.12.016 · Cited by 193 · see citing works

On Issues of Instance Selection

Liu, H., & Motoda, H. (2002) · Data Mining and Knowledge Discovery · doi:10.1023/a:1014056429969 · Cited by 191 · see citing works

Online Ensemble Learning

Oza, N. C. (2000) · Cited by 189 · see citing works

To Center or Not to Center: That Is Not the Question—An Ancillarity–Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Efficiency

Yu, Y., & Meng, X. (2011) · Journal of Computational and Graphical Statistics · doi:10.1198/jcgs.2011.203main · Cited by 187 · see citing works

Boosting Methods for Regression

Duffy, N., & Helmbold, D. P. (2002) · Machine Learning · doi:10.1023/a:1013685603443 · Cited by 186 · see citing works

Learning DNF in time 2Õ(n1/3)

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On the Bayes-risk consistency of regularized boosting methods

Lugosi, G., & Vayatis, N. (2004) · The Annals of Statistics · doi:10.1214/aos/1079120129 · Cited by 185 · see citing works

An improved boosting algorithm and its implications on learning complexity

Freund, Y. (1992) · Conference on Learning Theory · doi:10.1145/130385.130429 · Cited by 61 · see citing works

The Strength of Weak Learnability

Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600 · see citing works

Boosting Simple Learners

Alon, N., Gonen, A., Hazan, E., & Moran, S. (2021) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3406325.3451030 · Cited by 11 · see citing works

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

Fortino, G. (1997) · Archive of Doctoral Theses and Digital Collections (University of Calabria)

Perceptron, Winnow, and PAC Learning

Servedio, R. A. (2002) · SIAM Journal on Computing · doi:10.1137/s0097539798340928 · Cited by 8 · see citing works