Communications of the ACM
The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Experiments on multistrategy learning by meta-learning
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Error reduction through learning multiple descriptions
Machine Learning
A Study of Explanation-Based Methods for Inductive Learning
Machine Learning
Machine Learning
Machine Learning
THE WEIGHTED MAJORITY ALGORITHM (Supersedes 89-16)
THE WEIGHTED MAJORITY ALGORITHM (Supersedes 89-16)
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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
Machine Learning
A perspective view and survey of meta-learning
Artificial Intelligence Review
Combining Classifiers with Meta Decision Trees
Machine Learning
Distributed mining of classification rules
Knowledge and Information Systems
Combining Multiple Models with Meta Decision Trees
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Arbiter Meta-Learning with Dynamic Selection of Classifiers and Its Experimental Investigation
ADBIS '99 Proceedings of the Third East European Conference on Advances in Databases and Information Systems
Probing Knowledge in Distributed Data Mining
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Decision Committee Learning with Dynamic Integration of Classifiers
ADBIS-DASFAA '00 Proceedings of the East-European Conference on Advances in Databases and Information Systems Held Jointly with International Conference on Database Systems for Advanced Applications: Current Issues in Databases and Information Systems
Mining Several Data Bases With an Ensemble of Classifiers
DEXA '99 Proceedings of the 10th International Conference on Database and Expert Systems Applications
Instability of decision tree classification algorithms
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining tasks and methods: scalability
Handbook of data mining and knowledge discovery
Introduction to the Special Issue on Meta-Learning
Machine Learning
Lessons and Challenges from Mining Retail E-Commerce Data
Machine Learning
Genetic programming in classifying large-scale data: an ensemble method
Information Sciences: an International Journal - Special issue: Soft computing data mining
CLAIRE: A modular support vector image indexing and classification system
ACM Transactions on Information Systems (TOIS)
Local Feature Selection with Dynamic Integration of Classifiers
Fundamenta Informaticae - Intelligent Systems
Secure set intersection cardinality with application to association rule mining
Journal of Computer Security
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Privacy-preserving multi-party decision tree induction
International Journal of Business Intelligence and Data Mining
Grasp recognition for uncalibrated data gloves: A machine learning approach
Presence: Teleoperators and Virtual Environments
Feature Extraction for Dynamic Integration of Classifiers
Fundamenta Informaticae
Information-Theoretic Measures for Meta-learning
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Classification models for the prediction of clinicians' information needs
Journal of Biomedical Informatics
Computational Statistics & Data Analysis
Artificial Intelligence Review
On combined classifiers, rule induction and rough sets
Transactions on rough sets VI
Active rule learning using decision tree for resource management in Grid computing
Future Generation Computer Systems
Using decision tree models and diversity measures in the selection of ensemble classification models
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Execution engine of meta-learning system for KDD in multi-agent environment
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
Feature Extraction for Dynamic Integration of Classifiers
Fundamenta Informaticae
Local Feature Selection with Dynamic Integration of Classifiers
Fundamenta Informaticae - Intelligent Systems
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
Combining multiple predictive models using genetic algorithms
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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In this paper, wedescribe a general approach to scaling data mining applications thatwe have come to call meta-learning. Meta-Learningrefers to a general strategy that seeks to learn how to combine anumber of separate learning processes in an intelligent fashion. Wedesire a meta-learning architecture that exhibits two key behaviors.First, the meta-learning strategy must produce an accurate final classification system. This means that a meta-learning architecturemust produce a final outcome that is at least as accurate as aconventional learning algorithm applied to all available data.Second, it must be fast, relative to an individual sequential learningalgorithm when applied to massive databases of examples, and operatein a reasonable amount of time. This paper focussed primarily onissues related to the accuracy and efficacy of meta-learning as ageneral strategy. A number of empirical results are presenteddemonstrating that meta-learning is technically feasible in wide-area,network computing environments.