An Evolutionary Ensemble-Based Method for Rule Extraction with Distributed Data
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Learning and Meta-Learning for Coordination of Autonomous Unmanned VehiclesA Preliminary Analysis
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Active Testing Strategy to Predict the Best Classification Algorithm via Sampling and Metalearning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Towards systematic human brain data management using a data-brain based GLS-BI system
BI'10 Proceedings of the 2010 international conference on Brain informatics
Cross validation framework to choose amongst models and datasets for transfer learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
DS'10 Proceedings of the 13th international conference on Discovery science
A probabilistic risk analysis for multimodal entry control
Expert Systems with Applications: An International Journal
Empirical evaluation of ranking prediction methods for gene expression data classification
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Combining meta-learning and active selection of datasetoids for algorithm selection
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Uncertainty sampling-based active selection of datasetoids for meta-learning
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Using genetic algorithms to improve prediction of execution times of ML tasks
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
Towards an ontology of biomodelling
CMSB'12 Proceedings of the 10th international conference on Computational Methods in Systems Biology
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Clustering algorithm recommendation: a meta-learning approach
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Measuring feature distributions in sentiment classification
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Pairwise meta-rules for better meta-learning-based algorithm ranking
Machine Learning
Predicting execution time of machine learning tasks for scheduling
International Journal of Hybrid Intelligent Systems
A metric for unsupervised metalearning
Intelligent Data Analysis
Automatic selection of classification learning algorithms for data mining practitioners
Intelligent Data Analysis
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Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.