Meta-learning with kernels and similarity functions for planning of data mining workflows
نویسندگان
چکیده
We propose an intelligent data mining (DM) assistant that will combine planning and meta-learning to provide support to users of a virtual DM laboratory. A knowledge-driven planner will rely on a data mining ontology to plan the knowledge discovery workflow and determine the set of valid operators for each step of this workflow. A probabilistic metalearner will select the most appropriate operators by using relational similarity measures and kernel functions over records of past sessions meta-data stored in a DM experiments repository.
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Using Meta-mining to Support Data Mining Workflow Planning and Optimization
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