A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities

Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review

(2020) J. Carrasco et al.   Swarm and Evolutionary Computation

SPOCC: Scalable POssibilistic Classifier Combination - toward robust aggregation of classifiers

(2020) Mahmoud Albardan et al.   EXPERT SYSTEMS WITH APPLICATIONS

Explainable decision forest: Transforming a decision forest into an interpretable tree

(2020) Omer Sagi et al.   Information Fusion

Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection

(2019) Yuyan Wang et al.   APPLIED SOFT COMPUTING

Monotonic classification: An overview on algorithms, performance measures and data sets

(2019) José-Ramón Cano et al.   NEUROCOMPUTING

From Big to Smart Data: Iterative ensemble filter for noise filtering in Big Data classification

(2019) Diego García‐Gil et al.   INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS

Ensemble Selection based on Classifier Prediction Confidence

(2019) Tien Thanh Nguyen et al.   PATTERN RECOGNITION

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

(2019) Alejandro Barredo Arrieta et al.   Information Fusion

Combination of Classifiers With Optimal Weight Based on Evidential Reasoning

(2018) Zhun-Ga Liu et al.   IEEE TRANSACTIONS ON FUZZY SYSTEMS

Dynamic classifier selection: Recent advances and perspectives

(2018) Rafael M.O. Cruz et al.   Information Fusion

CNC-NOS: Class noise cleaning by ensemble filtering and noise scoring

(2018) Julián Luengo et al.   KNOWLEDGE-BASED SYSTEMS

Margin & diversity based ordering ensemble pruning

(2018) Huaping Guo et al.   NEUROCOMPUTING

Predicting reaction performance in C–N cross-coupling using machine learning

(2018) Derek T. Ahneman et al.   SCIENCE

Classifier Fusion With Contextual Reliability Evaluation

(2018) Zhunga Liu et al.   IEEE Transactions on Cybernetics

Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions

(2018) Henry Friday Nweke et al.   Information Fusion

Ensembles for feature selection: A review and future trends

(2018) Verónica Bolón-Canedo et al.   Information Fusion

Ensemble learning for data stream analysis: A survey

(2017) Bartosz Krawczyk et al.   Information Fusion

KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining

(2017) Isaac Triguero et al.   International Journal of Computational Intelligence Systems

Imputing missing value through ensemble concept based on statistical measures

(2017) Moslem Mohammadi Jenghara et al.   KNOWLEDGE AND INFORMATION SYSTEMS

Ensemble feature selection using bi-objective genetic algorithm

(2017) Asit K Das et al.   KNOWLEDGE-BASED SYSTEMS

Class Switching according to Nearest Enemy Distance for learning from highly imbalanced data-sets

(2017) Sergio Gónzalez et al.   PATTERN RECOGNITION

Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]

(2016) Ye Ren et al.   IEEE Computational Intelligence Magazine

Decision forest: Twenty years of research

(2016) Lior Rokach   Information Fusion

A robust multi-class AdaBoost algorithm for mislabeled noisy data

(2016) Bo Sun et al.   KNOWLEDGE-BASED SYSTEMS

RBoost: Label Noise-Robust Boosting Algorithm Based on a Nonconvex Loss Function and the Numerically Stable Base Learners

(2016) Qiguang Miao et al.   IEEE Transactions on Neural Networks and Learning Systems

ROSEFW-RF: The winner algorithm for the ECBDL’14 big data competition: An extremely imbalanced big data bioinformatics problem

(2015) Isaac Triguero et al.   KNOWLEDGE-BASED SYSTEMS

adabag: AnRPackage for Classification with Boosting and Bagging

(2015) Esteban Alfaro et al.   Journal of Statistical Software

Boosting instance selection algorithms

(2014) Nicolás García-Pedrajas et al.   KNOWLEDGE-BASED SYSTEMS

Dynamic selection of classifiers—A comprehensive review

(2014) Alceu S. Britto et al.   PATTERN RECOGNITION

Missing value imputation using decision trees and decision forests by splitting and merging records: Two novel techniques

(2013) Md. Geaur Rahman et al.   KNOWLEDGE-BASED SYSTEMS

Unsupervised feature selection with ensemble learning

(2013) Haytham Elghazel et al.   MACHINE LEARNING

Multi-class AdaBoost

(2013) Trevor Hastie et al.   Statistics and Its Interface

An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction

(2011) Koen W. De Bock et al.   EXPERT SYSTEMS WITH APPLICATIONS

Scalable and Parallel Boosting with MapReduce

(2011) Indranil Palit et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

(2011) M. Galar et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND RE

A measure of competence based on random classification for dynamic ensemble selection

(2011) Tomasz Woloszynski et al.   Information Fusion

An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes

(2011) Mikel Galar et al.   PATTERN RECOGNITION

A robust missing value imputation method for noisy data

(2010) Bing Zhu et al.   APPLIED INTELLIGENCE

Ensemble-based classifiers

(2009) Lior Rokach   ARTIFICIAL INTELLIGENCE REVIEW

Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power

(2009) Salvador García et al.   INFORMATION SCIENCES

Open-source machine learning: R meets Weka

(2008) Kurt Hornik et al.   COMPUTATIONAL STATISTICS

Data-driven decomposition for multi-class classification

(2007) Jie Zhou et al.   PATTERN RECOGNITION