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
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
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Multiknowledge for decision making
Knowledge and Information Systems
Entropies of fuzzy indiscrenibility relation and its operations
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Short communication: Uncertainty measures for fuzzy relations and their applications
Applied Soft Computing
EROS: Ensemble rough subspaces
Pattern Recognition
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Computational Statistics & Data Analysis
Analysis on classification performance of rough set based reducts
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Incremental learning with multiple classifier systems using correction filters for classification
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Research on rough set theory and applications in China
Transactions on rough sets VIII
Diversity measure for multiple classifier systems
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
A competitive ensemble pruning approach based on cross-validation technique
Knowledge-Based Systems
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Decision forests are a type of classification paradigm which combines a collection of decision trees for a classification task, instead of depending on a single tree. Improvement of accuracy and stability is observed in experiments and applications. Some novel techniques to construct decision forests are proposed based on rough set reduction in this paper. As there are a lot of reducts for some data sets, a series of decision trees can be trained with different reducts. Three methods to select decision trees or reducts are presented, and decisions from selected trees are fused with the plurality voting rule. The experiments show that random selection is the worst solution in the proposed methods. It is also found that input diversity maximization doesn't guarantee output diversity maximization. Hence it cannot guarantee a good classification performance in practice. Genetic algorithm based selective rough decision forests consistently get good classification accuracies compared with a single tree trained by raw data as well as the other two forest constructing methods.