Boosting a Weak Learning Algorithm by Majority

Turning Points and Classification

Piger, J. M. (2019) · Advanced studies in theoretical and applied econometrics · doi:10.1007/978-3-030-31150-6_18 · Cited by 10

Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data

Goyal, A., & Khiari, J. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207261 · Cited by 10

Study of swordfish fishing dynamics in the eastern Mediterranean by means of machine-learning approaches

Tserpes, G., Moutopoulos, D. K., Peristeraki, P. N., Katselis, G. N., & Koutsikopoulos, C. (2006) · Fisheries Research · doi:10.1016/j.fishres.2005.11.022 · Cited by 10

A Consistent Strategy for Boosting Algorithms

Lugosi, G., & Vayatis, N. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45435-7_21 · Cited by 10

On statistical learning via the lens of compressionPreprint

David, O., Moran, S., & Yehudayoff, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.03592 · Cited by 10

L2 Boosting in Kernel Regression 1Preprint

Park, B. U., Lee, Y. K., & Ha, S. (2008) · Cited by 10

Connecting Interpretability and Robustness in Decision Trees through SeparationPreprint

Moshkovitz, M., Yang, Y.-Y., & Chaudhuri, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.07048 · Cited by 10

Research on Classification Model of Fermented Milk Quality Control Based on Data Mining

Xiao, L., Xia, K., & Tian, H. (2019) · International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) · doi:10.1109/iciibms46890.2019.8991437 · Cited by 9

Automatic piecewise linear regression

von Ottenbreit, M., & Bin, R. D. (2024) · Computational Statistics · doi:10.1007/s00180-024-01475-4 · Cited by 9

Multiple Classifiers for Different Features in Timbre Estimation

Jiang, W., Zhang, X., Cohen, A., & Raś, Z. W. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-05183-8_14 · Cited by 9

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

Huang, K., Lu, S., Li, X., Feng, K., Chen, W., & Xia, Y. (2022) · Big Data Mining and Analytics · doi:10.26599/bdma.2022.9020028 · Cited by 9

An Improved AdaBoost Algorithm for Highly Imbalanced Datasets in the Co-Authorship Recommendation Problem

Quang, V. Đ., Viet, H. H., Long, V. H., & Khang, T. D. (2023) · IEEE Access · doi:10.1109/access.2023.3306783 · Cited by 9

Factors Influencing Educators’ Curiosity to Learn about VR Technologies in Education

Doz, D., Krajnčan, M., Jenko, M., & Vukovič, M. (2024) · TechTrends · doi:10.1007/s11528-024-00961-2 · Cited by 9

Handling Different Categories of Concept Drifts in Data Streams Using Distributed GP

Gianluigi Folino, A., & Papuzzo, G. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12148-7_7 · Cited by 9

A pattern classification approach for boosting with genetic algorithms

Yalabik, I., Yarman-Vural, F. T., Üçoluk, G., & Şehitoğlu, O. T. (2007) · International Symposium on Computer and Information Sciences · doi:10.1109/iscis.2007.4456870 · Cited by 9

Structured Sparse Boosting for Graph Classification

Fei, H., & Huan, J. (2014) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2629328 · Cited by 9

A Novel Dynamic Weight Neural Network Ensemble Model

Li, K., Liu, W., Zhao, K., Shao, M., & Liu, L. (2015) · International Journal of Distributed Sensor Networks · doi:10.1155/2015/862056 · Cited by 9

Semivariogram calculation optimization for object-oriented image classification

Balaguer, A., Hermosilla, T., Recio, J. A., & Ruiz, L. A. (2011) · Modelling in Science Education and Learning · doi:10.4995/msel.2011.3057 · Cited by 9

Aggregation of SVM Classifiers Using Sobolev Spaces

Loustau, S. (2008) · doi:10.5555/1390681.1442783 · Cited by 9

Boosting Formal Concepts to Discover Classification Rules

Meddouri, N., & Maddouri, M. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02568-6_51 · Cited by 9

A satellite schedulability prediction algorithm for EO SPS

Li, J., Li, J., Jing, N., Hu, W., & Chen, H. (2013) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2013.04.058 · Cited by 9

$L_2$ boosting in kernel regression

Park, B. U., Lee, Y. K., & Ha, S. (2009) · Bernoulli · doi:10.3150/08-bej160 · Cited by 9

An $L_{2}$-Boosting Algorithm for Estimation of a Regression Function

Bagirov, A., Clausen, C., & Köhler, M. (2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2039161 · Cited by 9

Cascaded multimodal biometric recognition framework

Baig, A., Bouridane, A., Kurugöllü, F., & Albesher, B. (2013) · IET Biometrics · doi:10.1049/iet-bmt.2012.0043 · Cited by 9

Quantification of gas concentrations in mixtures of known gases using an array of different tin-oxide sensors

Jervis, B. W., Desfieux, J., Jimenez, J., & Martinez, D. (2003) · IEE Proceedings - Science Measurement and Technology · doi:10.1049/ip-smt:20030324 · Cited by 9

On the Complexity of Simulating Auxiliary Input

Chen, Y.-H., Chung, K.-M., & Liao, J.-J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-78372-7_12 · Cited by 9

Ensemble convolutional neural networks for automatic fusion recognition of multi‐platform radar emitters

Zhou, Z., Huang, G., & Wang, X. (2019) · ETRI Journal · doi:10.4218/etrij.2017-0327 · Cited by 9

Predicting the Young’s Modulus of Rock Material Based on Petrographic and Rock Index Tests Using Boosting and Bagging Intelligence Techniques

Tsang, L., He, B., Rashid, A. S. A., Jalil, A. T., & Sabri, M. M. S. (2022) · Applied Sciences · doi:10.3390/app122010258 · Cited by 9

An Approach for Selective Ensemble Feature Selection Based on Rough Set Theory

Yang, Y., Wang, G., & He, K. (2007) · Springer eBooks · doi:10.1007/978-3-540-72458-2_64 · Cited by 9

PAC Analogues of Perceptron and Winnow Via Boosting the Margin

Servedio, R. A. (2002) · Machine Learning · doi:10.1023/a:1013633619373 · Cited by 9

Discussions of boosting papers, and rejoinders

Bartlett, P. L., Bickel, P. J., Bühlmann, P., Freund, Y., Friedman, J. H., Hastie, T. J., Jiang, W., Jordan, M. I., Koltchinskii, V. I., Lugosi, G., et al. (2004) · The Annals of Statistics · doi:10.1214/aos/1105988581 · Cited by 9

Adaptive Martingale Boosting

Long, P., & Servedio, R. A. (2008) · Cited by 9

Integrating boosting and stochastic attribute selection committees for further improving the performance of decision tree learning

Zheng, Z., Webb, G. I., & Ting, K. M. (2002) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/tai.1998.744846 · Cited by 9

Learning to identify new information

Schiffman, B., & McKeown, K. R. (2005) · Cited by 9

Parallel Perceptrons, Activation Margins and Imbalanced Training Set Pruning

Cantador, I., & Dorronsoro, J. R. (2005) · Lecture notes in computer science · doi:10.1007/11492542_6 · Cited by 9

ABC-LogitBoost for Multi-class ClassificationPreprint

Li, P. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0908.4144 · Cited by 9

Prediction based on averages over automatically induced learners: ensemble methods and Bayesian techniques

Hernández-Lobato, D. (2009) · Biblos-e Archivo (Universidad Autónoma de Madrid) · Cited by 9

Seeing Inside the Black Box: Using Diffusion Index Methodology to Construct Factor Proxies in Largescale Macroeconomic Time Series EnvironmentsPreprint

Armah, N. A., & Swanson, N. R. (2007) · SSRN Electronic Journal · doi:10.2139/ssrn.966438 · Cited by 9

Decision Trees

Podgorelec, V., & Zorman, M. (2009) · Encyclopedia of Complexity and Systems Science · doi:10.1007/978-0-387-30440-3_117 · Cited by 9

An Ensemble Approach for Prediction of Cardiovascular Disease Using Meta Classifier Boosting Algorithms

Patro, S. P., Padhy, N., & Sah, R. D. (2023) · International Journal of Data Warehousing and Mining · doi:10.4018/ijdwm.316145 · Cited by 8

Waste stabilization pond modelling using extreme gradient boosting machines

Ogarekpe, N. M., Agunwamba, J. C., Tenebe, I. T., Udodi, O. A., & Chinedu, A. D. (2024) · Water Practice & Technology · doi:10.2166/wpt.2024.277 · Cited by 8

Random Ordinality Ensembles: Ensemble methods for multi-valued categorical data

Ahmad, A., & Gavin Brown, B. (2014) · Information Sciences · doi:10.1016/j.ins.2014.10.064 · Cited by 8

Market segmentation and dynamic price discrimination in the U.S. airline industry

Gu, C. (2022) · Journal of Revenue and Pricing Management · doi:10.1057/s41272-022-00407-5 · Cited by 8

An Offline Weighted-Bagging Data-Driven Evolutionary Algorithm with Data Generation Based on Clustering

Guo, Z., Lin, S., Suo, R., & Zhang, X. (2023) · Mathematics · doi:10.3390/math11020431 · Cited by 8

Improving boosting methods with a stable loss function handling outliers

Chao, W., Li, B., Lei, W., & Pai, P. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-022-01766-6 · Cited by 8

Employing machine learning algorithm for properties of wood ceramics prediction: A case study of ammonia nitrogen adsorption capacity, apparent porosity, surface hardness and burn-off for wood ceramics

Jiang, W., Guo, X., Guan, Q., Zhang, Y., & Du, D. (2024) · Ceramics International · doi:10.1016/j.ceramint.2024.01.298 · Cited by 8

Accelerating FCM neural network classifier using graphics processing units with CUDA

Wang, L., Yang, B., Chen, Y., Chen, Z., & Sun, H. (2013) · Applied Intelligence · doi:10.1007/s10489-013-0450-8 · Cited by 8

Efficient and fast multi-view face detection based on feature transformation

Han, D., Kim, J., Ju, J., Lee, I., Cha, J., & Kim, J. (2014) · International Conference on Advanced Communication Technology · doi:10.1109/icact.2014.6779050 · Cited by 8

Performance enhancement of SVM ensembles using genetic algorithms in bankruptcy prediction

Kang, D.-K., & Kim, M.-J. (2010) · International Conference on Advanced Computer Theory and Engineering · doi:10.1109/icacte.2010.5579271 · Cited by 8

Dynamic Linear Solver Selection for Transient Simulations Using Machine Learning on Distributed Systems

Eller, P. R., Cheng, J.-R. C., & Maier, R. S. (2012) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw.2012.239 · Cited by 8

Evaluation of Ensembles for Feature Selection

Bolón‐Canedo, V., & Alonso‐Betanzos, A. (2018) · Intelligent systems reference library · doi:10.1007/978-3-319-90080-3_6 · Cited by 8

The Predictive Power of Heuristic Portfolios in Human Syllogistic Reasoning

Riesterer, N., Brand, D., & Ragni, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00111-7_35 · Cited by 8

Ensemble Detection and Analysis of Communities in Complex Networks

Chakraborty, T., Park, N., Agarwal, A., & Subrahmanian, V. S. (2020) · ACM/IMS Transactions on Data Science · doi:10.1145/3313374 · Cited by 8

ORBoost: An Orthogonal AdaBoost

Bostanian, Z., Boostani, R., Sabeti, M., & Mohammadi, M. (2022) · Intelligent Data Analysis · doi:10.3233/ida-205705 · Cited by 8

A Fundamental Overview of SOTA-Ensemble Learning Methods for Deep Learning: A Systematic Literature Review

Klaiber, M. (2021) · Science in Information Technology Letters · doi:10.31763/sitech.v2i2.549 · Cited by 9

Boosting Multiple Classifiers Constructed by Hybrid Discriminant Analysis

Tian, Q., Yu, J., & Huang, T. S. (2005) · Lecture notes in computer science · doi:10.1007/11494683_5 · Cited by 8

Geometric Bounds for Generalization in Boosting

Mannor, S., & Meir, R. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44581-1_30 · Cited by 8

A Meta-classifier Approach for Medical Diagnosis

Tsirogiannis, G. L., Frossyniotis, D., Nikita, K. S., & Stafylopatis, A. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24674-9_17 · Cited by 8

A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements

Srivastava, A., & Meade, A. J. (2015) · International Journal of Aerospace Engineering · doi:10.1155/2015/183712 · Cited by 8

Historical Land Use as a Feature for Image Classification

Recio, J. A., Hermosilla, T., Ruiz, L. Á., & Fernández-Sarría, A. (2011) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.77.4.377 · Cited by 8

A novel classification method using the combination of FDPS and flexible neural tree

Yang, B., Wang, L., Chen, Z., Chen, Y., & Sun, R. (2009) · Neurocomputing · doi:10.1016/j.neucom.2009.11.014 · Cited by 8

Learning with Actionable Attributes: Attention -- Boundary Cases!

Žliobaitė, I., & Pechenizkiy, M. (2010) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2010.140 · Cited by 8

Boosting with Noisy Data: Some Views from Statistical Theory

Jiang, W. (2004) · Neural Computation · doi:10.1162/089976604322860703 · Cited by 8

Coherence Functions for Multicategory Margin-based Classification Methods

Zhang, Z., Jordan, M. I., Li, W.-J., & Yeung, D. (2009) · Cited by 8

A Discussion of: "Process Consistency for AdaBoost" by Wenxin Jiang "On the Bayes-risk consistency of regularized boosting methods" by G´ abor Lugosi and Nicolas Vayatis "Statistical Behavior and Consistency of Classification Methods based on Convex Risk Minimization" by Tong Zhang

Freund, Y., & Schapire, R. E. (2004) · Cited by 8

A selective ensemble method based on K-means method

Liu, L., Wang, B., Zhong, Q., & Zeng, H. (2015) · International Conference on Computer Science and Network Technology (ICCSNT) · doi:10.1109/iccsnt.2015.7490832 · Cited by 8

Supervised methods for regrouping attributes in fuzzy rule-based classification systems

Slima, I. B., & Borgi, A. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1224-0 · Cited by 8

Data Mining Exploration System for Feature Selection Tasks

Suraj, Z., & Delimata (2006) · International Conference on Hybrid Information Technology (ICHIT) · doi:10.1109/ichit.2006.253500 · Cited by 8

Sample compression, learnability, and the Vapnik-Chervonenkis dimension

Warmuth, M. K. (1997) · Lecture notes in computer science · doi:10.1007/3-540-62685-9_1 · Cited by 8

Analysis of face detection based on skin color characteristic and AdaBoost algorithm

Li, P. X., Wang, H., Li, Y., & Liu, M. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1601/5/052019 · Cited by 8

Characterizing Global Land Cover Type and Seasonal Land Cover Dynamics at Moderate Spatial Resolution With MODIS Data

Friedl, M. A., Xiaoyang Zhang, A., & Strahler, A. H. (2010) · Remote sensing and digital image processing · doi:10.1007/978-1-4419-6749-7_31 · Cited by 8

Ensemble of Linear Perceptrons with Confidence Level Output

Hartono, P., & Hashimoto, S. (2005) · International Conference on Hybrid Intelligent Systems (HIS) · doi:10.1109/ichis.2004.41 · Cited by 8

A Robust Boosting Algorithm

Nock, R., & Lefaucheur, P. (2002) · Lecture notes in computer science · doi:10.1007/3-540-36755-1_27 · Cited by 8

Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis

Lu, Y., Tian, Q., Sanchez, M., Neary, J. L., Liu, F., & Wang, Y. (2007) · IEEE Multimedia · doi:10.1109/mmul.2007.4298456 · Cited by 8

Hybrid classification algorithms based on boosting and support vector machines

Maia, T. T., Braga, A. P., & de Carvalho, A. C. P. L. F. (2008) · Kybernetes · doi:10.1108/03684920810907814 · Cited by 8

Conjugate Direction Boosting

Lutz, R. W., & Bühlmann, P. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x113548 · Cited by 8

Learning with queries corrupted by classification noise

Jackson, J. C., Shamir, E., & Shwartzman, C. (1999) · Discrete Applied Mathematics · doi:10.1016/s0166-218x(99)00045-1 · Cited by 8

IMPROVING NEURAL NETWORK CLASSIFICATION USING FURTHER DIVISION OF RECOGNITION SPACE

Chen, Z., Wang, H., Abraham, A., Groşan, C., Yang, B., Chen, Y., & Wang, L. (2007) · Cited by 8

Bayesian model averaging

Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (2010) · Statistics, a series of textbooks and monographs · doi:10.1201/ebk1439836149-c9 · Cited by 8

Approaches in Machine Learning

Leeuwen, J. V. (2004) · Philips Research · doi:10.1007/978-94-017-0703-9_8 · Cited by 8

Multi-sensor data fusion and reliable multi-channel computation: unifying concepts and techniques

Parhami, B. (2002) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.1995.540649 · Cited by 8

Predicting the Density of Algae Communities using Local Regression Trees

Torgo, L. (2001) · Cited by 8

An Empirical Evaluation of Four Algorithms for Multi-Class Classification: Mart, ABC-Mart, Robust LogitBoost, and ABC-LogitBoostPreprint

Li, P. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1001.1020 · Cited by 8

Hand Gesture and Activity Recognition in Assisted Living Through Wearable Sensing and Computing

Zhu, C. (2011) · SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University) · Cited by 8

Gradient Boosted Binary Histogram Ensemble for Large-scale RegressionPreprint

Hang, H., Huang, T., Cai, Y., Yang, H., & Lin, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.01986 · Cited by 8

Single Phase Fault Detection of Induction Motor using Machine Learning Approaches

Turza, U. R., Das, V. D., Islam, S. H.-U., Pathan, M., Shahriar, M. S., Alam, M. S., Rahman, M. M., Islam, K. Z., Hossain, M. S., & Rahman, M. M. (2024) · IEEE International Conference in Power Engineering Applications (ICPEA) · doi:10.1109/icpea60617.2024.10498946 · Cited by 7

Reweighted-Boosting: A Gradient-Based Boosting Optimization Framework

He, G., Wang, Z., Tang, L., Yu, W., Nie, F., & Li, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3457764 · Cited by 7

A systematic review on the application of machine learning in carbon dioxide absorption in amine-related solvents

Law, J. H., Hussin, F., Jasser, M. B., & Aroua, M. K. (2024) · Reviews in Chemical Engineering · doi:10.1515/revce-2024-0047 · Cited by 8

Confidence ensembles: Tabular data classifiers on steroids

Zoppi, T., & Popov, P. T. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103126 · Cited by 7

IncA-DES: An incremental and adaptive dynamic ensemble selection approach using online K-d tree neighborhood search for data streams with concept drift

Barboza, E. V. L., de Almeida, P. R. L., Britto, A. D. S., Sabourin, R., & Cruz, R. M. O. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103272 · Cited by 7

Collider fingerprints of freeze-in dark matter produced during the fast expansion phase of Universe

Ghosh, A., Konar, P., & Show, S. (2025) · Physical review. D/Physical review. D. · doi:10.1103/pj7s-zhcr · Cited by 7

An Empirical Comparison of Two Boosting Algorithms on Real Data Sets Based on Analysis of Scientific Materials

Sun, X., & Zhou, H. (2011) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-23756-0_53 · Cited by 7

Improved Boosting Performance by Explicit Handling of Ambiguous Positive Examples

Kobetski, M., & Sullivan, J. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-12610-4_2 · Cited by 7

Subject-specific Bradley–Terry–Luce models with implicit variable selection

Casalicchio, G., Tutz, G., & Schauberger, G. (2015) · Statistical Modelling · doi:10.1177/1471082x15571817 · Cited by 7

A Novel Emotion Recognition Method Based on Ensemble Learning and Rough Set Theory

Yang, Y., & Wang, G. (2011) · International Journal of Cognitive Informatics and Natural Intelligence · doi:10.4018/ijcini.2011070104 · Cited by 7

Sentiment Classification for Chinese Netnews Comments Based on Multiple Classifiers Integration

Fan, W., Sun, S., & Song, G. (2011) · International Joint Conference on Computational Sciences and Optimization (CSO) · doi:10.1109/cso.2011.239 · Cited by 7

Artificial Intelligence Coreflooding Simulator for Special Core Data Analysis

Mathew, E. S., Tembely, M., AlAmeri, W. S., Al-Shalabi, E. W., & Shaik, A. R. (2021) · SPE Reservoir Evaluation & Engineering · doi:10.2118/202700-pa · Cited by 7

Addressing the inspection selection challenges of in-service pipeline girth weld using ensemble tree models

Li, H., Li, L., Chen, X., Zhou, Y., Li, Z., & Zhao, Z. (2023) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2023.107852 · Cited by 7

Multiple Observations HMM Learning by Aggregating Ensemble Models

Asadi, N., Mirzaei, A., & Haghshenas, E. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2280179 · Cited by 7

Soft-max boosting

Geist, M. (2015) · Machine Learning · doi:10.1007/s10994-015-5491-2 · Cited by 7

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