Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A perspective view and survey of meta-learning
Artificial Intelligence Review
Model selection via meta-learning: a comparative study
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Introduction to the Special Issue on Meta-Learning
Machine Learning
Mindful: A framework for Meta-INDuctive neuro-FUzzy Learning
Information Sciences: an International Journal
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
GRASP forest: a new ensemble method for trees
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Meta-learning experiences with the mindful system
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Exploring the way for meta-learning with the MINDFUL system
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
A cloud-based workflow management solution for collaborative analytics
ICSOC'11 Proceedings of the 2011 international conference on Service-Oriented Computing
A survey of intelligent assistants for data analysis
ACM Computing Surveys (CSUR)
Efficient feature size reduction via predictive forward selection
Pattern Recognition
A feature subset selection algorithm automatic recommendation method
Journal of Artificial Intelligence Research
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Common inductive learning strategies offer the tools for knowledge acquisition, but possess some inherent limitations due to the use of fixed bias during the learning process. To overcome limitations of such base-learning approaches, a novel research trend is oriented to explore the potentialities of meta-learning, which is oriented to the development of mechanisms based on a dynamical search of bias. This could lead to an improvement of the base-learner performance on specific learning tasks, by profiting of the accumulated past experience. As a significant set of I/O data is needed for efficient base-learning, appropriate meta-data characterization is of crucial importance for useful meta-learning. In order to characterize meta-data, firstly a collection of meta-features discriminating among different base-level tasks should be identified. This paper focuses on the characterization of meta-data, through an analysis of meta-features that can capture the properties of specific tasks to be solved at base level. This kind of approach represents a first step toward the development of a meta-learning system, capable of suggesting the proper bias for base-learning different specific task domains.