Boosting a Weak Learning Algorithm by Majority

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

Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336

Wrappers for feature subset selection

Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 9,096

Experiments with a new boosting algorithm

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

Pattern Recognition and Neural Networks

Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,500

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

Selection of relevant features and examples in machine learning

Blum, A. L., & Pat Langley, B. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00063-5 · Cited by 3,294

A Short Introduction to Boosting

Freund, Y., & Schapire, R. E. (1999) · Cited by 2,991

Global land cover mapping from MODIS: algorithms and early results

Friedl, M. A., McIver, D. K., Hodges, J. C. F., Xiaoyang Zhang, A., Muchoney, D. M., Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A. B., et al. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(02)00078-0 · Cited by 2,686

Ensemble-based classifiers

Rokach, L. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-009-9124-7 · Cited by 2,657

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 2,662

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

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 2,574

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

Prasad, A. M., Iverson, L. R., & Liaw, A. (2006) · Ecosystems · doi:10.1007/s10021-005-0054-1 · Cited by 1,987

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 2,101

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

Extreme learning machines: a survey

Huang, G.-B., Wang, D. H., & Lan, Y. (2011) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-011-0019-y · Cited by 1,922

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 1,542

A brief introduction to boosting

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

A comprehensive survey of fitness approximation in evolutionary computation

Jin, Y. (2003) · Soft Computing · doi:10.1007/s00500-003-0328-5 · Cited by 1,252

Explaining AdaBoost

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

Foundations of Machine Learning

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

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 1,262

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

Breiman, L. (1998) · The Annals of Statistics · doi:10.1214/aos/1024691079 · Cited by 1,124

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 930

Boosting With the L 2 Loss

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

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 845

Unsupervised Models for Named Entity Classification

Collins, M., & Singer, Y. (1999) · Cited by 819

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 797

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 769

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

Buch, N., Velastín, S. A., & Orwell, J. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2119372 · Cited by 670

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 623

Classification and Regression Trees, Bagging, and Boosting

Sutton, C. D. (2005) · Handbook of statistics · doi:10.1016/s0169-7161(04)24011-1 · Cited by 618

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

Wang, J., Jiang, C., Zhang, H., Ren, Y., Chen, K.-C., & Hanzo, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2965856 · Cited by 568

Neural Probabilistic Language Models

Bengio, Y., Schwenk, H., Senécal, J.-S., Morin, F., & Gauvain, J. (2006) · Springer eBooks · doi:10.1007/3-540-33486-6_6 · Cited by 557

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 579

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 523

Random Forests and Adaptive Nearest Neighbors

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

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 460

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 460

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 481

Empirical Asset Pricing via Machine Learning

Gu, S., Kelly, B., & Xiu, D. (2018) · National Bureau of Economic Research · doi:10.3386/w25398 · Cited by 423

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 413

Data Mining With R: Learning By Case Studies

Torgo, L. (2010) · Cited by 398

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

Bagging, Boosting and Ensemble Methods

Bühlmann, P. (2011) · Springer eBooks · doi:10.1007/978-3-642-21551-3_33 · Cited by 380

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 394

High-Performance Rotation Invariant Multiview Face Detection

Huang, C., Ai, H., Li, Y., & Lao, S. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1011 · Cited by 388

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 374

A simple, fast, and effective rule learner

Cohen, W. W., & Singer, Y. (1999) · Cited by 341

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 356

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

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 335

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 329

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

Computational learning theory

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

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 325

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 323

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 359

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 313

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

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 305

Boosting Neural Networks

Schwenk, H., & Bengio, Y. (2000) · Neural Computation · doi:10.1162/089976600300015178 · Cited by 299

Ensembles of Learning Machines

Valentini, G., & Masulli, F. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45808-5_1 · Cited by 290

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

An Adaptive Version of the Boost by Majority Algorithm

Freund, Y. (2001) · Machine Learning · doi:10.1023/a:1010852229904 · Cited by 288

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 276

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 334

Using output codes to boost multiclass learning problems

Schapire, R. E. (1997) · Cited by 259

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 266

Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension

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

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

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

Dai, H. A., Veer, L. V., Lamb, J. R., He, Y. D., Mao, M., Fine, B. M., Bernards, R., van de Vijver, M. J., Deutsch, P., Sachs, A. B., et al. (2005) · Cancer Research · doi:10.1158/0008-5472.can-04-3953 · Cited by 251

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 271

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

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 248

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

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

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

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 227

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 233

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

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 235

DATA STREAM MINING A Practical Approach

Bifet, A., & Kirkby, R. (2009) · Cited by 221

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 237

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

Margin Analysis of the LVQ Algorithm

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

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

Using Correspondence Analysis to Combine Classifiers

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

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

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

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

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

Bayesian Approaches to Failure Prediction for Disk Drives

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

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 195

On Issues of Instance Selection

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

Online Ensemble Learning

Oza, N. C. (2000) · Cited by 189

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

Boosting Methods for Regression

Duffy, N., & Helmbold, D. P. (2002) · Machine Learning · doi:10.1023/a:1013685603443 · Cited by 187

Learning DNF in time 2Õ(n1/3)

Klivans, A. R., & Servedio, R. A. (2003) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2003.07.007 · Cited by 185

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

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

The Strength of Weak Learnability

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

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

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