The cascade-correlation learning architecture
Advances in neural information processing systems 2
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An approach to the automatic design of multiple classifier systems
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Boosting Algorithms for Parallel and Distributed Learning
Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Distributed learning with bagging-like performance
Pattern Recognition Letters
Creating Ensembles of Classifiers
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Distributed Pasting of Small Votes
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Tree induction vs. logistic regression: a learning-curve analysis
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A new ensemble diversity measure applied to thinning ensembles
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
Distributed classification in peer-to-peer networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental classifier based on a local credibility criterion
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Using classifier ensembles to label spatially disjoint data
Information Fusion
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Constructing ensembles of symbolic classifiers
International Journal of Hybrid Intelligent Systems - Hybrid Intelligent systems in Ensembles
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Boosting and measuring the performance of ensembles for a successful database marketing
Expert Systems with Applications: An International Journal
A divide-and-conquer recursive approach for scaling up instance selection algorithms
Data Mining and Knowledge Discovery
Computational Statistics & Data Analysis
Artificial Intelligence Review
Boosting lite: handling larger datasets and slower base classifiers
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Hierarchical distributed data classification in wireless sensor networks
Computer Communications
Analysis of bagging ensembles of fuzzy models for premises valuation
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Comparison of bagging, boosting and stacking ensembles applied to real estate appraisal
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Detecting and ordering salient regions
Data Mining and Knowledge Discovery
Neural network classifers in arrears management
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
A modular reduction method for k-NN algorithm with self-recombination learning
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Designing multiple classifier systems for face recognition
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
A scalable approach to simultaneous evolutionary instance and feature selection
Information Sciences: an International Journal
Classifying tag relevance with relevant positive and negative examples
Proceedings of the 21st ACM international conference on Multimedia
A supervised learning approach to the ensemble clustering of genes
International Journal of Data Mining and Bioinformatics
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Bagging and boosting are two popular ensemble methods that typically achieve better accuracy than a single classifier. These techniques have limitations on massive data sets, because the size of the data set can be a bottleneck. Voting many classifiers built on small subsets of data ("pasting small votes") is a promising approach for learning from massive data sets, one that can utilize the power of boosting and bagging. We propose a framework for building hundreds or thousands of such classifiers on small subsets of data in a distributed environment. Experiments show this approach is fast, accurate, and scalable.