Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning
Machine Learning
Error reduction through learning multiple descriptions
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Arbitrating among competing classifiers using learned referees
Knowledge and Information Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Using Correspondence Analysis to Combine Classifiers
Machine Learning
Combining Classifiers with Meta Decision Trees
Machine Learning
Machine Learning
Characterizing Model Erros and Differences
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Mixtures of Experts Estimate A Posteriori Probabilities
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
An Evaluation of Grading Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
A theory of classifier combination: the neural network approach
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Distributed Data Mining in Peer-to-Peer Networks
IEEE Internet Computing
Multiple classifiers by constrained minimization
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Adaptive mixtures of local experts
Neural Computation
Issues in stacked generalization
Journal of Artificial Intelligence Research
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The collective decision making and, in particular, the collective recognition is treated as the problem of joint application of multiple classifier decisions. The decisions are made about the class of an entity, situation, image, etc. The joint decision is used to improve quality of the final decision by aggregation and coordination of different classifier decisions using a metalevel algorithm. The studies in the field of collective recognition, which were started in the middle of the 1950s, find wide application in practice during the last decade. Since they are used for solving complex large-scale applied problems, the interest of both theoretical scientists and engineers is focused on them. A new impetus for the studies was given by the recent development of embedded distributed structures involving ensembles of intellectual sensors that make decisions under uncertainties on the base of limited local information. The final decision of high quality, in particular, the decision of higher aggregation level, is made by combining local classifier decisions on the metalevel. There are dozens of recent publications proposing new ideas and new approaches and algorithms of collective recognition. Unfortunately, some papers rediscover results publushed several decades before. The goal of this review is to present the main ideas of collective recognition and to outline the status of researches basing on the original source works. The review covers the period from the 1950s, when the first ideas and methods appeared, up to present time.