The Strength of Weak Learnability
Machine Learning
Boosting a weak learning algorithm by majority
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Neural networks and the bias/variance dilemma
Neural Computation
Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Rough set methods and applications: new developments in knowledge discovery in information systems
Rough set methods and applications: new developments in knowledge discovery in information systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Reduction algorithms based on discernibility matrix: the ordered attributes method
Journal of Computer Science and Technology
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Journal of the American Society for Information Science and Technology
Multiknowledge for decision making
Knowledge and Information Systems
On fuzzy-rough sets approach to feature selection
Pattern Recognition Letters
Automatic identification of music performers with learning ensembles
Artificial Intelligence
Toward a successful CRM: variable selection, sampling, and ensemble
Decision Support Systems
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Neural network ensembles: evaluation of aggregation algorithms
Artificial Intelligence
Using diversity of errors for selecting members of a committee classifier
Pattern Recognition
NeC4.5: Neural Ensemble Based C4.5
IEEE Transactions on Knowledge and Data Engineering
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Stopping criteria for ensemble of evolutionary artificial neural networks
Applied Soft Computing
Empirical study on weighted voting multiple classifiers
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Constructing rough decision forests
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Ensembling local learners ThroughMultimodal perturbation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Collective-agreement-based pruning of ensembles
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
The model of fuzzy variable precision rough sets
IEEE Transactions on Fuzzy Systems
Artificial Intelligence Review
Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications
International Journal of Approximate Reasoning
Selection-fusion approach for classification of datasets with missing values
Pattern Recognition
Stability analysis on rough set based feature evaluation
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Expert Systems with Applications: An International Journal
Diverse reduct subspaces based co-training for partially labeled data
International Journal of Approximate Reasoning
Ensemble pruning using harmony search
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
A competitive ensemble pruning approach based on cross-validation technique
Knowledge-Based Systems
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Malware detection by pruning of parallel ensembles using harmony search
Pattern Recognition Letters
An effective ensemble pruning algorithm based on frequent patterns
Knowledge-Based Systems
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Ensemble learning is attracting much attention from pattern recognition and machine learning domains for good generalization. Both theoretical and experimental researches show that combining a set of accurate and diverse classifiers will lead to a powerful classification system. An algorithm, called FS-PP-EROS, for selective ensemble of rough subspaces is proposed in this paper. Rough set-based attribute reduction is introduced to generate a set of reducts, and then each reduct is used to train a base classifier. We introduce an accuracy-guided forward search and post-pruning strategy to select part of the base classifiers for constructing an efficient and effective ensemble system. The experiments show that classification accuracies of ensemble systems with accuracy-guided forward search strategy will increase at first, arrive at a maximal value, then decrease in sequentially adding the base classifiers. We delete the base classifiers added after the maximal accuracy. The experimental results show that the proposed ensemble systems outperform bagging and random subspace methods in terms of accuracy and size of ensemble systems. FS-PP-EROS can keep or improve the classification accuracy with very few base classifiers, which leads to a powerful and compact classification system.