Instance-Based Learning Algorithms
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Error estimation by series association for neural network systems
Neural Computation
Methods for combining experts' probability assessments
Neural Computation
Machine Learning
Machine Learning
Stacking Bagged and Dagged Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Machine Learning
A meta-learning approach for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Synthesizing High-Frequency Rules from Different Data Sources
IEEE Transactions on Knowledge and Data Engineering
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Stacking for Misclassification Cost Performance
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Experiments with Projection Learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
Trainable Multiple Classifier Schemes for Handwritten Character Recognition
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
A refinement approach to handling model misfit in text categorization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Improving accuracy in word class tagging through the combination of machine learning systems
Computational Linguistics
Exploiting structural information for semi-structured document categorization
Information Processing and Management: an International Journal
An experimental comparative study of web mining methods for recommender systems
DIWED'06 Proceedings of the 6th WSEAS International Conference on Distance Learning and Web Engineering
Researching on Multi-net Systems Based on Stacked Generalization
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
On the Performance of Stacked Generalization Classifiers
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Boosting and measuring the performance of ensembles for a successful database marketing
Expert Systems with Applications: An International Journal
Guidelines to Select Machine Learning Scheme for Classification of Biomedical Datasets
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Exploiting structural information for semi-structured document categorization
Information Processing and Management: an International Journal
GA-stacking: Evolutionary stacked generalization
Intelligent Data Analysis
Stacking MF networks to combine the outputs provided by RBF networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Association rule mining: models and algorithms
Association rule mining: models and algorithms
A meta learning approach: classification by cluster analysis
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
Fusion of similarity measures for time series classification
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Graph-Based model-selection framework for large ensembles
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Boosting-based ensemble learning with penalty profiles for automatic Thai unknown word recognition
Computers & Mathematics with Applications
Ensembles of strong learners for multi-cue classification
Pattern Recognition Letters
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In order to rank the performance of machine learning algorithms, many researchers conduct experiments on benchmark data sets. Since most learning algorithms have domain-specific parameters, it is a popular custom to adapt these parameters to obtain a ...