Boosting a Weak Learning Algorithm by Majority

A Survey of Distributed Classification Based Ensemble Data Mining Methods

Mokeddem, D., & Belbachir, H. (2009) · Journal of Applied Sciences · doi:10.3923/jas.2009.3739.3745 · Cited by 7

A fast and effective Extreme learning machine algorithm without tuning

Er, M. J., Shao, Z., & Wang, N. (2014) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2014.6889397 · Cited by 7

A new ensemble method for gold mining problems: Predicting technology transfer

Kwon, O. (2011) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2011.06.004 · Cited by 7

Label Alteration to Improve Underwater Mine Classification

Williams, D. P. (2010) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2010.2088106 · Cited by 7

Enhanced Algorithm Performance for Classification Based on Hyper Surface using Bagging and Adaboost

He, Q., Zhuang, F., Zhao, X., & Shi, Z. (2007) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2007.4370775 · Cited by 7

Ensembles of general regression neural networks for short-term electricity demand forecasting

Dudek, G. (2017) · International Scientific Conference on Electric Power Engineering · doi:10.1109/epe.2017.7967256 · Cited by 7

Boosted decision trees for behaviour mining of concurrent programmes

Avros, R., Dudka, V., Křena, B., Letko, Z., Pluháčková, H., Ur, S., Vojnar, T., & Volkovich, Z. (2017) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4268 · Cited by 7

Face Recognition Performance in Facing Pose Variation

Gunawan, A. A. S., & Prasetyo, R. A. (2017) · CommIT (Communication and Information Technology) Journal · doi:10.21512/commit.v11i1.1847 · Cited by 7

A Distributionally Robust Boosting Algorithm

Blanchet, J., Zhang, F., Kang, Y., & Hu, Z. (2019) · Winter Simulation Conference · doi:10.1109/wsc40007.2019.9004804 · Cited by 7

Additive Ensemble Neural Networks

Park, M., Lee, S., Hwang, S., & Kim, D. (2020) · IEEE Access · doi:10.1109/access.2020.3003748 · Cited by 7

Three papers on boosting: an introduction

Koltchinskii, V. I., & Yu, B. (2004) · The Annals of Statistics · doi:10.1214/aos/1079120127 · Cited by 7

A novel ensembling method to boost performance of neural networks

Chakraborty, M., Biswas, S. K., & Purkayastha, B. (2019) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2019.1610799 · Cited by 7

A New Cost-sensitive SVM Algorithm for Imbalanced Dataset

Hengyu, Z. (2021) · International Conference on Consumer Electronics and Computer Engineering (ICCECE) · doi:10.1109/iccece51280.2021.9342072 · Cited by 7

Partial Occam's Razor and its applications

Domingo, C., Tsukiji, T., & Watanabe, O. (1997) · Information Processing Letters · doi:10.1016/s0020-0190(97)00169-5 · Cited by 7

Cultural Evolution of Ensemble Learning for Problem Solving

Reynolds, R. G., Peng, B., & Alomari, R. S. (2006) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2006.1688435 · Cited by 7

Learning with continuous experts using drifting games

Mukherjee, I., & Schapire, R. E. (2010) · Theoretical Computer Science · doi:10.1016/j.tcs.2010.04.004 · Cited by 7

Bagging-like effects for decision trees and neural nets in protein secondary structure prediction

Chawla, N. V., Moore, T. E., Bowyer, K. W., Hall, L., Springer, C., & Kegelmeyer, P. (2001) · Cited by 7

On Boosting with Optimal Poly-Bounded Distributions

Bshouty, N. H., & Gavinsky, D. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44581-1_32 · Cited by 7

Application of evolutionary computation and neural network hybrids for breast cancer classification using mammogram and history data

Land, W. H., Masters, T. D., Lo, J. Y.-C., & McKee, D. W. (2002) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2001.934320 · Cited by 7

Drifting Games and Brownian Motion

Freund, Y., & Opper, M. (2002) · Journal of Computer and System Sciences · doi:10.1006/jcss.2001.1802 · Cited by 7

Recent developments in discriminant analysis from an information geometric point of view

Eguchi, S. (2001) · Cited by 7

Boosting with the Logistic Loss is ConsistentPreprint

Telgarsky, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.2648 · Cited by 7

QSAR modeling: prediction of biological activity from chemical structure

Liaw, A. I., & Svetnik, V. (2014) · Wiley eBooks · doi:10.1002/9781118763070.ch3 · Cited by 7

Speaker recognition using adaptively boosted decision tree classifier

Foo, S. W., & Lim, E. G. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.1005700 · Cited by 7

Data Stream Mining

Read, J., & Bifet, A. (2014) · IGI Global eBooks · doi:10.4018/978-1-4666-5202-6.ch061 · Cited by 7

An empirical investigation of tree ensembles in biometrics and bioinformatics research.

Ma, Y. (2007) · doi:10.33915/etd.9336 · Cited by 7

A general approach to progressive learningPreprint

Vogelstein, J. T., Helm, H. S., Mehta, R., Dey, J. K., Yang, W., Tower, B., LeVine, W., Larson, J., White, C., & Priebe, C. E. (2020) · arXiv (Cornell University) · Cited by 7

Construct and evaluate the classification models of six types of geological hazards in Bijie city, Guizhou province, ChinaPreprint

Shi, J., Zhang, J., & Shen, C. (2020) · doi:10.5194/nhess-2020-124 · Cited by 7

Nonlinear ensemble techniques and reliability to estimate oxygen transfer efficiency of rectangular venturi flumes for free flow

Panwar, D., & Tiwari, N. K. (2024) · ISH Journal of Hydraulic Engineering · doi:10.1080/09715010.2024.2387143 · Cited by 6

Explainable AI model for PDFMal detection based on gradient boosting model

Elattar, M., Younes, A., Gad, I., & Elkabani, I. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10314-y · Cited by 6

Machine learning-driven prediction of eye irritation toxicity: Integration of in silico and in vitro study

Chen, S., Wang, J., Lü, H., Wan, Y., Yu, Y., & Wang, Y. (2025) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2025.117457 · Cited by 6

Using deep learning to construct microcalcification clusters in a mammography prediction model

Hsu, P.-Y., Xu, J.-L., Chen, L.-L., & Chen, M. (2025) · Innovation and Emerging Technologies · doi:10.1142/s2737599425500306 · Cited by 6

The diagnostic value of urinary exosomes in prostate cancer: a prospective multicenter study

Li, Y., Pan, D., Wang, Z., Ji, J., Jin, X., Chen, X., Lu, W., Wang, L., & Wang, F. (2025) · BMC Urology · doi:10.1186/s12894-025-01941-1 · Cited by 6

Forecasting currency covariances using machine learning tree-based algorithms with low and high prices

Bejger, S., & Fiszeder, P. (2021) · Przegląd Statystyczny Statistical Review · doi:10.5604/01.3001.0015.5582 · Cited by 6

Evaluating Air Quality Status in Chicago: Application of Street View Imagery and Urban Climate Sensors

Jiao, J., Choi, S. J., Wang, H., & Farahi, A. (2023) · Environmental Modeling & Assessment · doi:10.1007/s10666-023-09894-1 · Cited by 6

NOx Emission Prediction of Diesel Vehicles in Deep Underground Mines Using Ensemble Methods

Kotyla, M., Banasiewicz, A., Krot, P. V., Śliwiński, P., & Zimroz, R. (2024) · Electronics · doi:10.3390/electronics13061095 · Cited by 6

Local lateral connectivity is sufficient for replicating cortex-like topographical organization in deep neural networks

Qian, X., Dehghani, A., Farahani, A., & Bashivan, P. (2026) · Nature Communications · doi:10.1038/s41467-026-70065-3 · Cited by 8

Feature fusion of palmprint and face via tensor analysis and curvelet transform

Xu, X., Guan, X., Zhang, D., Zhang, X., Deng, W., & Wang, Z. (2012) · Opto-Electronics Review · doi:10.2478/s11772-012-0015-0 · Cited by 6

Multimodal approach to feature extraction for image and signal learning problems

Eads, D. R., Williams, S., Theiler, J. P., Porter, R. B., Harvey, N. R., Perkins, S. J., Brumby, S. P., & David, N. A. (2004) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.506611 · Cited by 6

Efficient Improvement for Adaboost Based Object Detection

Luo, S., & Ye, X. (2009) · International Conference on Computational Intelligence and Natural Computing (CINC) · doi:10.1109/cinc.2009.88 · Cited by 6

A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy Approach

Ponti, M. A., Papa, J. P., & Levada, A. L. M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25085-9_69 · Cited by 6

PAC-Bayesian risk bounds for group-analysis sparse regression by exponential weighting

Luu, D. T., Fadili, J. M., & Chesneau, C. (2018) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2018.12.004 · Cited by 6

Accumulator Based Arbitration Model for both Supervised and Reinforcement Learning Inspired by Prefrontal Cortex

Osawa, M., Ashihara, Y., Seno, T., Imai, M., & Kurihara, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70087-8_63 · Cited by 6

Accuracy based weighted aging ensemble (AB-WAE) — Algorithm for data stream classification

Woźniak, M. (2017) · International Conference on Soft Computing & Machine Intelligence (ISCMI) · doi:10.1109/iscmi.2017.8279591 · Cited by 6

Learning from unequally reliable blind ensembles of classifiers

Traganitis, P. A., Pagés-Zamora, A., & Giannakis, G. B. (2017) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2017.8308613 · Cited by 6

Face Detection and Tracking Using Raspberry Pi based on Haar Cascade Classifier

Huang, X., & Cao, X. S. (2022) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac57282.2022.10023612 · Cited by 6

A hybrid quantum annealing method for generating ensemble classifiers

Yulianti, L. P., Trisetyarso, A., Santoso, J., & Surendro, K. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101831 · Cited by 6

Learning adversarially robust kernel ensembles with kernel average pooling

Bashivan, P., Bayat, R., Ibrahim, A., Dehghani, A., & Ren, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126017 · Cited by 7

On Evolving Multi-agent FX Traders

Loginov, A., & Heywood, M. I. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-45523-4_17 · Cited by 6

Text Categorization Using SVMs with Rocchio Ensemble for Internet Information Classification

Xu, X., Zhang, B., & Zhong, Q. (2005) · Lecture notes in computer science · doi:10.1007/11534310_107 · Cited by 6

Adaptive Learning of Nominal Concepts for Supervised Classification

Meddouri, N., & Maddouri, M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15387-7_16 · Cited by 6

Convergence and Consistency of Regularized Boosting With Weakly Dependent Observations

Lozano, A. C., Kulkarni, S. R., & Schapire, R. E. (2013) · IEEE Transactions on Information Theory · doi:10.1109/tit.2013.2287726 · Cited by 6

Automated quantitative measurements and associated error covariances for planetary image analysis

Tar, P., Thacker, N. A., Gilmour, J. D., & Jones, M. A. (2015) · Advances in Space Research · doi:10.1016/j.asr.2015.03.043 · Cited by 6

A Comparison between Hybrid and Non-hybrid Classifiers in Diagnosis of Induction Motor Faults

dos Santos, S. P., & Costa, J. A. F. (2008) · IEEE International Conference on Computational Science and Engineering (CSE) · doi:10.1109/cse.2008.60 · Cited by 6

Adaptive Algorithm for Classifying Launch and Recovery of the HH-60H Seahawk

Srivastava, A., Meade, A. J., & Needham, J. (2009) · Journal of Aerospace Computing Information and Communication · doi:10.2514/1.37910 · Cited by 6

On the Small Sample Performance of Boosted Classifiers

Li, W., Gao, X., Zhu, Y., Ramesh, V., & Boult, T. E. (2005) · 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.2005.258 · Cited by 6

A methodology to select particle morpho-chemical characteristics to use in source apportionment of particulate matter from livestock houses

Cambra‐López, M., Hermosilla, T., Aarnink, A. J. A., & Ogink, N. W. M. (2011) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2011.11.002 · Cited by 6

A ROBUST BOOSTING METHOD FOR MISLABELED DATA

Sano, N., Suzuki, H., & Koda, M. (2004) · Journal of the Operations Research Society of Japan · doi:10.15807/jorsj.47.182 · Cited by 6

Using support vector machine and dynamic parameter encoding to enhance global optimization

Zheng, Z. T., Chen, X., Liu, C., & Huang, K. (2015) · Engineering Optimization · doi:10.1080/0305215x.2015.1057056 · Cited by 6

Emg pattern classification using hierarchical network based on boosting approach

Okamoto, M., Matsubara, Y., Shima, K., & Tsuji, T. (2009) · Cited by 6

Attributes Regrouping in Fuzzy Rule Based Classification Systems: An Intra-Classes Approach

Borgi, A., Kalaï, R., & Zgaya, H. (2018) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2018.8612802 · Cited by 6

Application of Differential Evolution Algorithm Based on Mixed Penalty Function Screening Criterion in Imbalanced Data Integration Classification

Gao, Y., Wang, K., Gao, C., Shen, Y., & Li, T. (2019) · Mathematics · doi:10.3390/math7121237 · Cited by 6

Heat transfer performance and prediction of open pulsating heat pipe for self-cooling cutting tool

Wu, Z. W., Bao, H., Xing, Y., & Liu, L. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09796-8 · Cited by 6

Learning with Ensemble of Linear Perceptrons

Hartono, P., & Hashimoto, S. (2005) · Lecture notes in computer science · doi:10.1007/11550907_19 · Cited by 6

Linear Regression and Its Cousins

Kühn, M., & Johnson, K. (2013) · Springer eBooks · doi:10.1007/978-1-4614-6849-3_6 · Cited by 8

Sorting-Based Dynamic Classifier Ensemble Selection

Yan, Y., Yin, X.-C., Wang, Z., Yin, X., Yang, C. H., & Hao, H. (2013) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2013.138 · Cited by 6

Boosted adaptive filters

Kari, D., Mirza, A. H., Khan, F., Özkan, H., & Kozat, S. S. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2018.07.012 · Cited by 7

Sample compression schemes for VC classes

Moran, S., & Yehudayoff, A. (2016) · Information Theory and Applications Workshop (ITA) · doi:10.1109/ita.2016.7888187 · Cited by 6

Advances in boosting

Schapire, R. E. (2002) · Uncertainty in Artificial Intelligence · Cited by 6

Support Vector Committee machines

Martinez, D., & Millérioux, G. (2000) · Cited by 6

Ensemble Learning with Biased Classifiers: The Triskel Algorithm

Heß, A., Khoussainov, R., & Kushmerick, N. (2005) · Lecture notes in computer science · doi:10.1007/11494683_23 · Cited by 6

Toward a Computational Theory of Data Acquisition and Truthing

Stork, D. G. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44581-1_13 · Cited by 6

Algorithms for manipulation of level sets of nonparametric density estimates

Klemelä, J. (2005) · Computational Statistics · doi:10.1007/bf02789708 · Cited by 6

Aggregating Density Estimators: An Empirical Study

Bourel, M., & Ghattas, B. (2013) · Open Journal of Statistics · doi:10.4236/ojs.2013.35040 · Cited by 6

Designing neural network architectures for pattern recognition

Kubát, M. (2000) · The Knowledge Engineering Review · doi:10.1017/s0269888900002022 · Cited by 6

Learning DNF from random walks

Bshouty, N. H., Mossel, E., O’Donnell, R., & Servedio, R. A. (2004) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.2003.1238193 · Cited by 6

Disaggregation Analysis and Statistical Learning: An Integrated Framework for Multicriteria Decision Support

Doumpos, M., & Zopounidis, C. (2010) · Applied optimization · doi:10.1007/978-3-540-92828-7_7 · Cited by 6

Bagging and Boosting

Veaux, R. D. D. (2005) · Encyclopedia of Biostatistics · doi:10.1002/0470011815.b2a13003 · Cited by 6

Information theoretic combination of classifiers with application to face detection

Meynet, J. (2007) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-3951 · Cited by 6

Recursive Bias Estimation and $L_2$ BoostingPreprint

Cornillon, P. A., Hengartner, N., & Matzner-Løber, É. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0801.4629 · Cited by 6

Fast high-dimensional Bayesian classification and clustering

Nia, V. P. (2009) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-4482 · Cited by 6

Automatic Voicemail Summarisation for Mobile Messaging

Koumpis, K. (2002) · Cited by 6

Geometric primitive feature extraction - concepts, algorithms, and applicationsPreprint

Prasad, D. K. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.3885 · Cited by 6

On multi-armed bandits theory and applications

Aziz, M. (2019) · doi:10.17760/d20316241 · Cited by 6

Optimization in logical analysis of data

Bonates, T. (2007) · Rutgers University Community Repository (Rutgers University) · doi:10.7282/t32n52pz · Cited by 6

Smart adaptive ensemble model for multiclass imbalanced nonstationary data streams

Palli, A. S., Jaafar, J., Saad, M. H. M., Mokhtar, A. A., Gomes, H. M., Soomro, A. A., & Gilal, A. R. (2025) · Scientific Reports · doi:10.1038/s41598-025-05122-w · Cited by 5

Enhanced prediction of fly ash concrete compressive strength using machine learning models

Alsaadawi, M. M., Hamed, A. K., & Elshaarawy, M. K. (2026) · Engineering Computations · doi:10.1108/ec-08-2024-0774 · Cited by 5

A Survey of Deep Learning for Time Series Forecasting: Taxonomy, Analysis and Future Directions

Guo, Q., Zhao, B., Song, M., & Zhong, G. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3671378 · Cited by 6

Big Data Analyzing Techniques in Mathematical House Price Prediction Model

Yang, J. (2022) · IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) · doi:10.1109/eebda53927.2022.9744970 · Cited by 5

Classification: From expert to ensemble to crowd

Saitta, L. (2014) · Intelligenza Artificiale · doi:10.3233/ia-140065 · Cited by 5

Ensemble of Linear Experts as an Interpretable Piecewise Linear Classifier

Hartono, P. (2008) · Cited by 5

Weakly supervised object detection from remote sensing images via self-attention distillation and instance-aware mining

Yang, P., Zhou, S., Wang, L., & Yang, G. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17237-1 · Cited by 5

Short-term electrical load forecasting based on multi-granularity time augmented learning

Chu, J., Wei, C., Li, J., & Lu, X. (2024) · Electrical Engineering · doi:10.1007/s00202-024-02698-w · Cited by 5

On Optimality of Mallows Model Averaging

Peng, J., Li, Y., & Yang, Y. (2024) · Journal of the American Statistical Association · doi:10.1080/01621459.2024.2402566 · Cited by 5

Study on Logging Identification of Sandstone-Type Uranium Deposits Based on Ensemble Learning in the Songliao Basin in Northeast China

Xiao, K., Xu, Y., Yang, Y., Hu, X., Luo, Q., Duan, Z., Jiao, C., Chen, M., & Yin, D. (2024) · Nuclear Science and Engineering · doi:10.1080/00295639.2024.2437916 · Cited by 5

Quantum DNF Learnability Revisited

Jackson, J. C., Tamon, C., & Yamakami, T. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45655-4_63 · Cited by 5

An application of sensor networks with data mining to patient controlled analgesia

Hu, Y., Jan, R., Wang, K., Tseng, Y., Ku, T.-H., Yang, S., & Wu, H.-S. (2010) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/health.2010.5556545 · Cited by 5

Handwritten Chinese Similar Characters Recognition Based On AdaBoost

Zhang, B., & Jin, L. (2006) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2006.4347361 · Cited by 5

Network Based Prediction Model for Genomics Data Analysis

Huang, Y., & Wang, P. (2012) · Statistics in Biosciences · doi:10.1007/s12561-012-9056-7 · Cited by 5

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