Characterizing the applicability of classification algorithms using meta-level learning
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Control strategies in HTN planning: theory versus practice
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Characterization of Classification Algorithms
EPIA '95 Proceedings of the 7th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Towards Process-Oriented Tool Support for Knowledge Discovery in Databases
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Fusion of Meta-knowledge and Meta-data for Case-Based Model Selection
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
IEEE Transactions on Knowledge and Data Engineering
The Data Mining Advisor: Meta-learning at the Service of Practitioners
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
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One of the problems of Knowledge Discovery in Databases (KDD) is the lack of user support for solving KDD problems. Current DataMining (DM) systems enable the user to manually design workflows but this becomes difficult when there are too many operators to choose from or the workflow's size is too large. Therefore we propose to use auto-experimentation based on ontological planning to provide the users with automatic generated workflows as well as rankings for workflows based on several criteria (execution time, accuracy, etc.). Moreover autoexperimentation will help to validate the generated workflows and to prune and reduce their number. Furthermore we will use mixed-initiative planning to allow the users to set parameters and criteria to limit the planning search space as well as to guide the planner towards better workflows.