Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Optimal linear combinations of neural networks
Neural Networks
Machine Learning - Special issue on inductive transfer
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting in the limit: maximizing the margin of learned ensembles
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Direct optimization of margins improves generalization in combined classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Approximation algorithms for maximization problems arising in graph partitioning
Journal of Algorithms
Machine Learning
Linear Programming Boosting via Column Generation
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Column Generation Algorithm For Boosting
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Learning Ensembles from Bites: A Scalable and Accurate Approach
The Journal of Machine Learning Research
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Genetic algorithm based selective neural network ensemble
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
The Interplay of Optimization and Machine Learning Research
The Journal of Machine Learning Research
Greedy regression ensemble selection: Theory and an application to water quality prediction
Information Sciences: an International Journal
Bayesian Collaborative Predictors for General User Modeling Tasks
Neural Information Processing
Resampling-based selective clustering ensembles
Pattern Recognition Letters
Collective-agreement-based pruning of ensembles
Computational Statistics & Data Analysis
Incremental construction of classifier and discriminant ensembles
Information Sciences: an International Journal
Focused Ensemble Selection: A Diversity-Based Method for Greedy Ensemble Selection
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A Heuristic for Fair Correlation-Aware Resource Placement
SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
Decision Templates Based RBF Network for Tree-Structured Multiple Classifier Fusion
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A fast ensemble pruning algorithm based on pattern mining process
Data Mining and Knowledge Discovery
Artificial Intelligence Review
Selection of decision stumps in bagging ensembles
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Ensemble pruning via individual contribution ordering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse ensembles using weighted combination methods based on linear programming
Pattern Recognition
Mr.KNN: soft relevance for multi-label classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Analyzing classification methods in multi-label tasks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Voting-averaged combination method for regressor ensemble
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
A probabilistic model of classifier competence for dynamic ensemble selection
Pattern Recognition
Enabling fast prediction for ensemble models on data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Ensemble pruning via base-classifier replacement
WAIM'11 Proceedings of the 12th international conference on Web-age information management
A new metric for greedy ensemble pruning
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Empirical comparison of four classifier fusion strategies for positive-versus-negative ensembles
Proceedings of the South African Institute of Computer Scientists and Information Technologists Conference on Knowledge, Innovation and Leadership in a Diverse, Multidisciplinary Environment
Eigenclassifiers for combining correlated classifiers
Information Sciences: an International Journal
Multi-label classification models for sustainable flood retention basins
Environmental Modelling & Software
Margin distribution based bagging pruning
Neurocomputing
A double pruning algorithm for classification ensembles
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Expert pruning based on genetic algorithm in regression problems
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Ensemble pruning using harmony search
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Energy-Based metric for ensemble selection
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
The build of n-Bits Binary Coding ICBP Ensemble System
Neurocomputing
Margin optimization based pruning for random forest
Neurocomputing
Introduction to the special issue on learning from multi-label data
Machine Learning
A competitive ensemble pruning approach based on cross-validation technique
Knowledge-Based Systems
Diversity regularized ensemble pruning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Margin-based ordered aggregation for ensemble pruning
Pattern Recognition Letters
INFORMS Journal on Computing
Malware detection by pruning of parallel ensembles using harmony search
Pattern Recognition Letters
Using Bayesian networks for selecting classifiers in GP ensembles
Information Sciences: an International Journal
An effective ensemble pruning algorithm based on frequent patterns
Knowledge-Based Systems
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An ensemble is a group of learning models that jointly solve a problem. However, the ensembles generated by existing techniques are sometimes unnecessarily large, which can lead to extra memory usage, computational costs, and occasional decreases in effectiveness. The purpose of ensemble pruning is to search for a good subset of ensemble members that performs as well as, or better than, the original ensemble. This subset selection problem is a combinatorial optimization problem and thus finding the exact optimal solution is computationally prohibitive. Various heuristic methods have been developed to obtain an approximate solution. However, most of the existing heuristics use simple greedy search as the optimization method, which lacks either theoretical or empirical quality guarantees. In this paper, the ensemble subset selection problem is formulated as a quadratic integer programming problem. By applying semi-definite programming (SDP) as a solution technique, we are able to get better approximate solutions. Computational experiments show that this SDP-based pruning algorithm outperforms other heuristics in the literature. Its application in a classifier-sharing study also demonstrates the effectiveness of the method.