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Versatile and Efficient Meta-Learning Architecture: Knowledge Representation and Management in Computational Intelligence

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2 Author(s)
Grabczewski, K. ; Dept. of Informatics, Nicolaus Copernicus Univ., Toruri ; Jankowski, N.

There are many data mining systems derived from machine learning, neural network, statistics and other fields. Most of them are dedicated to some particular algorithms or applications. Unfortunately, their architectures are still too naive to provide satisfactory background for advanced meta-learning problems. In order to efficiently perform sophisticated meta-level analysis, we need a very versatile, easily expandable system (in many independent aspects), which uniformly deals with different kinds of models and models with very complex structures of models (not only committees but also much more hierarchic models). Meta-level techniques must provide mechanisms facilitating optimization of computation time and memory consumption. This article presents requirements and their motivations for an advanced data mining system, efficient not only in model construction for given data, but also in meta-learning. Some particular solutions to significant problems are presented. The newly proposed advanced meta-learning architecture has been implemented in our new data analysis system.

Published in:

Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on

Date of Conference:

March 1 2007-April 5 2007