UCI++: Improved Support for Algorithm Selection Using Datasetoids
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Support feature machine for DNA microarray data
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
A priori and a posteriori machine learning and nonlinear artificial neural networks
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?
Expert Systems with Applications: An International Journal
Combining Uncertainty Sampling methods for supporting the generation of meta-examples
Information Sciences: an International Journal
Detecting Fake Medical Web Sites Using Recursive Trust Labeling
ACM Transactions on Information Systems (TOIS)
Flexible Algorithm Selection Framework for Large Scale Metalearning
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Towards a method for automatically evolving bayesian network classifiers
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A feature subset selection algorithm automatic recommendation method
Journal of Artificial Intelligence Research
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
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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.